{"cells":[{"metadata":{"trusted":true},"cell_type":"code","source":"import numpy as np #导入NumPy数学工具箱\nimport pandas as pd #导入Pandas数据处理工具箱\ndf_bank = pd.read_csv(\"../input/bank-customer/BankCustomer.csv\") # 读取文件\ndf_bank.head() # 显示文件前5行","execution_count":53,"outputs":[{"output_type":"execute_result","execution_count":53,"data":{"text/plain":"              Name  Gender  Age     City  Tenure  ProductsNo  HasCard  \\\n0         Kan Jian    Male   37  Tianjin       3           2        1   \n1      Xue Baochai  Female   39  Beijing       9           1        1   \n2           Mao Xi  Female   32  Beijing       9           1        1   \n3  Zheng Nengliang  Female   37  Tianjin       0           2        1   \n4          Zhi Fen    Male   55  Tianjin       4           3        1   \n\n   ActiveMember  Credit  AccountBal  Salary  Exited  \n0             1     634    31937.37  137062       0  \n1             1     556    18144.95  110194       0  \n2             1     803    10378.09  236311       1  \n3             1     778    25564.01  129910       1  \n4             0     547     3235.61  136976       1  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Name</th>\n      <th>Gender</th>\n      <th>Age</th>\n      <th>City</th>\n      <th>Tenure</th>\n      <th>ProductsNo</th>\n      <th>HasCard</th>\n      <th>ActiveMember</th>\n      <th>Credit</th>\n      <th>AccountBal</th>\n      <th>Salary</th>\n      <th>Exited</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Kan Jian</td>\n      <td>Male</td>\n      <td>37</td>\n      <td>Tianjin</td>\n      <td>3</td>\n      <td>2</td>\n      <td>1</td>\n      <td>1</td>\n      <td>634</td>\n      <td>31937.37</td>\n      <td>137062</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Xue Baochai</td>\n      <td>Female</td>\n      <td>39</td>\n      <td>Beijing</td>\n      <td>9</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>556</td>\n      <td>18144.95</td>\n      <td>110194</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Mao Xi</td>\n      <td>Female</td>\n      <td>32</td>\n      <td>Beijing</td>\n      <td>9</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>803</td>\n      <td>10378.09</td>\n      <td>236311</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Zheng Nengliang</td>\n      <td>Female</td>\n      <td>37</td>\n      <td>Tianjin</td>\n      <td>0</td>\n      <td>2</td>\n      <td>1</td>\n      <td>1</td>\n      <td>778</td>\n      <td>25564.01</td>\n      <td>129910</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Zhi Fen</td>\n      <td>Male</td>\n      <td>55</td>\n      <td>Tianjin</td>\n      <td>4</td>\n      <td>3</td>\n      <td>1</td>\n      <td>0</td>\n      <td>547</td>\n      <td>3235.61</td>\n      <td>136976</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"import matplotlib.pyplot as plt #导入matplotlib画图工具箱\nimport seaborn as sns #导入seaborn画图工具箱\n# 显示不同特征的分布情况\nfeatures=[ 'City', 'Gender','Age','Tenure', \n           'ProductsNo', 'HasCard', 'ActiveMember', 'Exited']\nfig=plt.subplots(figsize=(15,15))\nfor i, j in enumerate(features):\n    plt.subplot(4, 2, i+1)\n    plt.subplots_adjust(hspace = 1.0)\n    sns.countplot(x=j,data = df_bank)\n    plt.title(\"No. of costumers\")","execution_count":55,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1080x1080 with 8 Axes>","image/png":"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把二元类别文本数字化\ndf_bank['Gender'].replace(\"Female\",0,inplace = True)\ndf_bank['Gender'].replace(\"Male\",1,inplace=True)\n# 显示数字类别\nprint(\"Gender unique values\",df_bank['Gender'].unique())\n# 把多元类别转换成多个二元哑变量，然后贴回原始数据集\nd_city = pd.get_dummies(df_bank['City'], prefix = \"City\")\ndf_bank = [df_bank, d_city]\ndf_bank = pd.concat(df_bank, axis = 1)\n# 构建特征和标签集合\ny = df_bank ['Exited']\nX = df_bank.drop(['Name', 'Exited','City'], axis=1)\nX.head() #显示新的特征集","execution_count":57,"outputs":[{"output_type":"stream","text":"Gender unique values [1 0]\n","name":"stdout"},{"output_type":"execute_result","execution_count":57,"data":{"text/plain":"   Gender  Age  Tenure  ProductsNo  HasCard  ActiveMember  Credit  AccountBal  \\\n0       1   37       3           2        1             1     634    31937.37   \n1       0   39       9           1        1             1     556    18144.95   \n2       0   32       9           1        1             1     803    10378.09   \n3       0   37       0           2        1             1     778    25564.01   \n4       1   55       4           3        1             0     547     3235.61   \n\n   Salary  City_Beijing  City_Shanghai  City_Tianjin  \n0  137062             0              0             1  \n1  110194             1              0             0  \n2  236311             1              0             0  \n3  129910             0              0             1  \n4  136976             0              0             1  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Gender</th>\n      <th>Age</th>\n      <th>Tenure</th>\n      <th>ProductsNo</th>\n      <th>HasCard</th>\n      <th>ActiveMember</th>\n      <th>Credit</th>\n      <th>AccountBal</th>\n      <th>Salary</th>\n      <th>City_Beijing</th>\n      <th>City_Shanghai</th>\n      <th>City_Tianjin</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>37</td>\n      <td>3</td>\n      <td>2</td>\n      <td>1</td>\n      <td>1</td>\n      <td>634</td>\n      <td>31937.37</td>\n      <td>137062</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0</td>\n      <td>39</td>\n      <td>9</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>556</td>\n      <td>18144.95</td>\n      <td>110194</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0</td>\n      <td>32</td>\n      <td>9</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>803</td>\n      <td>10378.09</td>\n      <td>236311</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0</td>\n      <td>37</td>\n      <td>0</td>\n      <td>2</td>\n      <td>1</td>\n      <td>1</td>\n      <td>778</td>\n      <td>25564.01</td>\n      <td>129910</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1</td>\n      <td>55</td>\n      <td>4</td>\n      <td>3</td>\n      <td>1</td>\n      <td>0</td>\n      <td>547</td>\n      <td>3235.61</td>\n      <td>136976</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"from sklearn.model_selection import train_test_split #拆分数据集\nX_train, X_test, y_train, y_test = train_test_split(X, y, \n                                   test_size=0.2, random_state=0)","execution_count":59,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"from sklearn.linear_model import LogisticRegression # 导入Sklearn模型\nlr = LogisticRegression() # 逻辑回归模型\nhistory = lr.fit(X_train,y_train) # 训练机器\nprint(\"逻辑回归测试集准确率 {:.2f}%\".format(lr.score(X_test,y_test)*100))","execution_count":61,"outputs":[{"output_type":"stream","text":"逻辑回归测试集准确率 78.35%\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"import keras # 导入Keras库\nfrom keras.models import Sequential # 导入Keras序贯模型\nfrom keras.layers import Dense # 导入Keras密集连接层\nann = Sequential() # 创建一个序贯ANN(Artifical Neural Network)模型\nann.add(Dense(units=12, input_dim=12, activation = 'relu')) # 添加输入层\nann.add(Dense(units=24, activation = 'relu')) # 添加隐层\nann.add(Dense(units=1, activation = 'sigmoid')) # 添加输出层\nann.summary() # 显示网络模型(这个语句不是必须的)","execution_count":63,"outputs":[{"output_type":"stream","text":"Model: \"sequential_3\"\n_________________________________________________________________\nLayer (type)                 Output Shape              Param #   \n=================================================================\ndense_7 (Dense)              (None, 12)                156       \n_________________________________________________________________\ndense_8 (Dense)              (None, 24)                312       \n_________________________________________________________________\ndense_9 (Dense)              (None, 1)                 25        \n=================================================================\nTotal params: 493\nTrainable params: 493\nNon-trainable params: 0\n_________________________________________________________________\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"from IPython.display import SVG\nfrom keras.utils.vis_utils import model_to_dot\nSVG(model_to_dot(ann,show_shapes = True ).create(prog='dot', format='svg'))","execution_count":64,"outputs":[{"output_type":"execute_result","execution_count":64,"data":{"text/plain":"<IPython.core.display.SVG object>","image/svg+xml":"<svg height=\"405pt\" viewBox=\"0.00 0.00 302.00 304.00\" width=\"403pt\" 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loss = 'binary_crossentropy', #损失函数  \n            metrics = ['acc'])       #评估指标","execution_count":65,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"history = ann.fit(X_train, y_train, # 指定训练集\n                  epochs=30,        # 指定训练的轮次\n                  batch_size=64,    # 指定数据批量\n                  validation_data=(X_test, y_test)) #指定验证集,这里为了简化模型，直接用测试集数据进行验证","execution_count":67,"outputs":[{"output_type":"stream","text":"Train on 8000 samples, validate on 2000 samples\nEpoch 1/30\n8000/8000 [==============================] - 0s 40us/step - loss: 582.7277 - acc: 0.5874 - val_loss: 21.1787 - val_acc: 0.5140\nEpoch 2/30\n8000/8000 [==============================] - 0s 21us/step - loss: 20.8019 - acc: 0.6504 - val_loss: 15.6053 - val_acc: 0.7085\nEpoch 3/30\n8000/8000 [==============================] - 0s 21us/step - loss: 19.6293 - acc: 0.6650 - val_loss: 37.3416 - val_acc: 0.7660\nEpoch 4/30\n8000/8000 [==============================] - 0s 21us/step - loss: 15.5447 - acc: 0.6730 - val_loss: 13.9860 - val_acc: 0.7820\nEpoch 5/30\n8000/8000 [==============================] - 0s 21us/step - loss: 19.6147 - acc: 0.6755 - val_loss: 19.1928 - val_acc: 0.7875\nEpoch 6/30\n8000/8000 [==============================] - 0s 21us/step - loss: 25.6364 - acc: 0.6779 - val_loss: 15.0183 - val_acc: 0.5095\nEpoch 7/30\n8000/8000 [==============================] - 0s 21us/step - loss: 20.6823 - acc: 0.6795 - val_loss: 8.9003 - val_acc: 0.5190\nEpoch 8/30\n8000/8000 [==============================] - 0s 21us/step - loss: 16.7798 - acc: 0.6823 - val_loss: 8.7221 - val_acc: 0.5060\nEpoch 9/30\n8000/8000 [==============================] - 0s 21us/step - loss: 26.7788 - acc: 0.6851 - val_loss: 18.2920 - val_acc: 0.5540\nEpoch 10/30\n8000/8000 [==============================] - 0s 20us/step - loss: 16.2260 - acc: 0.6842 - val_loss: 8.1268 - val_acc: 0.3515\nEpoch 11/30\n8000/8000 [==============================] - 0s 21us/step - loss: 13.2382 - acc: 0.6751 - val_loss: 15.1087 - val_acc: 0.7900\nEpoch 12/30\n8000/8000 [==============================] - 0s 21us/step - loss: 34.1614 - acc: 0.6812 - val_loss: 15.8879 - val_acc: 0.7905\nEpoch 13/30\n8000/8000 [==============================] - 0s 21us/step - loss: 17.9369 - acc: 0.6819 - val_loss: 14.7933 - val_acc: 0.7880\nEpoch 14/30\n8000/8000 [==============================] - 0s 21us/step - loss: 21.7021 - acc: 0.6725 - val_loss: 5.5230 - val_acc: 0.7290\nEpoch 15/30\n8000/8000 [==============================] - 0s 21us/step - loss: 20.1159 - acc: 0.6865 - val_loss: 9.3506 - val_acc: 0.7910\nEpoch 16/30\n8000/8000 [==============================] - 0s 21us/step - loss: 10.0233 - acc: 0.6810 - val_loss: 6.5140 - val_acc: 0.7905\nEpoch 17/30\n8000/8000 [==============================] - 0s 21us/step - loss: 16.4918 - acc: 0.6801 - val_loss: 27.4985 - val_acc: 0.7890\nEpoch 18/30\n8000/8000 [==============================] - 0s 21us/step - loss: 15.0708 - acc: 0.6795 - val_loss: 21.3877 - val_acc: 0.4760\nEpoch 19/30\n8000/8000 [==============================] - 0s 21us/step - loss: 21.0504 - acc: 0.6852 - val_loss: 33.7847 - val_acc: 0.7925\nEpoch 20/30\n8000/8000 [==============================] - 0s 21us/step - loss: 15.5329 - acc: 0.6860 - val_loss: 5.2579 - val_acc: 0.7920\nEpoch 21/30\n8000/8000 [==============================] - 0s 21us/step - loss: 14.3794 - acc: 0.6867 - val_loss: 15.0699 - val_acc: 0.7910\nEpoch 22/30\n8000/8000 [==============================] - 0s 21us/step - loss: 16.8865 - acc: 0.6811 - val_loss: 9.6431 - val_acc: 0.5100\nEpoch 23/30\n8000/8000 [==============================] - 0s 21us/step - loss: 26.3815 - acc: 0.6855 - val_loss: 3.0084 - val_acc: 0.7370\nEpoch 24/30\n8000/8000 [==============================] - 0s 21us/step - loss: 14.2019 - acc: 0.6766 - val_loss: 4.9898 - val_acc: 0.4805\nEpoch 25/30\n8000/8000 [==============================] - 0s 21us/step - loss: 17.1574 - acc: 0.6779 - val_loss: 10.9024 - val_acc: 0.7920\nEpoch 26/30\n8000/8000 [==============================] - 0s 21us/step - loss: 11.4836 - acc: 0.6990 - val_loss: 13.3095 - val_acc: 0.7905\nEpoch 27/30\n8000/8000 [==============================] - 0s 21us/step - loss: 14.9429 - acc: 0.6827 - val_loss: 20.9964 - val_acc: 0.7875\nEpoch 28/30\n8000/8000 [==============================] - 0s 21us/step - loss: 13.3197 - acc: 0.6849 - val_loss: 34.7365 - val_acc: 0.7915\nEpoch 29/30\n8000/8000 [==============================] - 0s 21us/step - loss: 17.5607 - acc: 0.6885 - val_loss: 4.1137 - val_acc: 0.7900\nEpoch 30/30\n8000/8000 [==============================] - 0s 21us/step - loss: 13.8934 - acc: 0.6889 - val_loss: 9.5654 - val_acc: 0.7895\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"# 这段代码参考《Python深度学习》一书中的学习曲线的实现\ndef show_history(history): # 显示训练过程中的学习曲线\n    loss = history.history['loss']\n    val_loss = history.history['val_loss']\n    epochs = range(1, len(loss) + 1)\n    plt.figure(figsize=(12,4))\n    plt.subplot(1, 2, 1)\n    plt.plot(epochs, loss, 'bo', label='Training loss')\n    plt.plot(epochs, val_loss, 'b', label='Validation loss')\n    plt.title('Training and validation loss')\n    plt.xlabel('Epochs')\n    plt.ylabel('Loss')\n    plt.legend()\n    acc = history.history['acc']\n    val_acc = history.history['val_acc']\n    plt.subplot(1, 2, 2)\n    plt.plot(epochs, acc, 'bo', label='Training acc')\n    plt.plot(epochs, val_acc, 'b', label='Validation acc')\n    plt.title('Training and validation accuracy')\n    plt.xlabel('Epochs')\n    plt.ylabel('Accuracy')\n    plt.legend()\n    plt.show()\nshow_history(history) # 调用这个函数，并将神经网络训练历史数据作为参数输入","execution_count":69,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 864x288 with 2 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\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"y_pred = ann.predict(X_test,batch_size=10) # 预测测试集的标签\ny_pred = np.round(y_pred) # 四舍五入，将分类概率值转换成0/1整数值","execution_count":70,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"from sklearn.metrics import classification_report # 导入分类报告\ndef show_report(X_test, y_test, y_pred): # 定义一个函数显示分类报告\n    if y_test.shape != (2000,1):\n        y_test = y_test.values # 把Panda series转换成Numpy array\n        y_test = y_test.reshape((len(y_test),1)) # 转换成与y_pred相同的形状 \n    print(classification_report(y_test,y_pred,labels=[0, 1])) #调用分类报告   ","execution_count":71,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"from sklearn.metrics import confusion_matrix # 导入混淆矩阵\ndef show_matrix(y_test, y_pred): # 定义一个函数显示混淆矩阵\n    cm = confusion_matrix(y_test,y_pred) # 调用混淆矩阵\n    plt.title(\"ANN Confusion Matrix\") # 标题\n    sns.heatmap(cm,annot=True,cmap=\"Blues\",fmt=\"d\",cbar=False) # 热力图设定\n    plt.show() # 显示混淆矩阵","execution_count":73,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"**特征缩放（Feature Scaling）**"},{"metadata":{"trusted":true},"cell_type":"code","source":"# mean = X_train.mean(axis=0) # 计算训练集均值\n# X_train -= mean # 训练集减去训练集均值\n# std = X_train.std(axis=0) # 计算训练集方差\n# X_train /= std # 训练集除以训练集标准差\n# X_test -= mean # 测试集减去训练集均值\n# X_test /= std # 测试集减去训练集均值","execution_count":76,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"from sklearn.preprocessing import StandardScaler # 导入特征缩放器\nsc = StandardScaler() # 特征缩放器\nX_train = sc.fit_transform(X_train) # 拟合并应用于训练集\nX_test = sc.transform (X_test) # 训练集结果应用于测试集","execution_count":77,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"from sklearn.linear_model import LogisticRegression\nlr = LogisticRegression() # 逻辑回归模型\nhistory = lr.fit(X_train,y_train) # 训练机器\nprint(\"逻辑回归测试集准确率 {:.2f}%\".format(lr.score(X_test,y_test)*100))","execution_count":79,"outputs":[{"output_type":"stream","text":"逻辑回归测试集准确率 80.50%\n","name":"stdout"}]},{"metadata":{},"cell_type":"markdown","source":"**特征工程后重新调用神经网络**"},{"metadata":{"trusted":true},"cell_type":"code","source":"history = ann.fit(X_train, y_train, # 指定训练集\n                  epochs=30,        # 指定训练的轮次\n                  batch_size=64,    # 指定数据批量\n                  validation_data=(X_test, y_test)) #指定验证集\ny_pred = ann.predict(X_test,batch_size=10) # 预测测试集的标签\ny_pred = np.round(y_pred) # 将分类概率值转换成0/1整数值","execution_count":81,"outputs":[{"output_type":"stream","text":"Train on 8000 samples, validate on 2000 samples\nEpoch 1/30\n8000/8000 [==============================] - 0s 24us/step - loss: 0.4697 - acc: 0.7996 - val_loss: 0.4291 - val_acc: 0.8190\nEpoch 2/30\n8000/8000 [==============================] - 0s 21us/step - loss: 0.4196 - acc: 0.8195 - val_loss: 0.4116 - val_acc: 0.8265\nEpoch 3/30\n8000/8000 [==============================] - 0s 21us/step - loss: 0.3992 - acc: 0.8310 - val_loss: 0.3977 - val_acc: 0.8390\nEpoch 4/30\n8000/8000 [==============================] - 0s 21us/step - loss: 0.3855 - acc: 0.8388 - val_loss: 0.3890 - val_acc: 0.8470\nEpoch 5/30\n8000/8000 [==============================] - 0s 22us/step - loss: 0.3758 - acc: 0.8435 - val_loss: 0.3835 - val_acc: 0.8480\nEpoch 6/30\n8000/8000 [==============================] - 0s 21us/step - loss: 0.3692 - acc: 0.8474 - val_loss: 0.3778 - val_acc: 0.8515\nEpoch 7/30\n8000/8000 [==============================] - 0s 21us/step - loss: 0.3640 - acc: 0.8501 - val_loss: 0.3731 - val_acc: 0.8535\nEpoch 8/30\n8000/8000 [==============================] - 0s 21us/step - loss: 0.3602 - acc: 0.8509 - val_loss: 0.3709 - val_acc: 0.8545\nEpoch 9/30\n8000/8000 [==============================] - 0s 21us/step - loss: 0.3573 - acc: 0.8516 - val_loss: 0.3703 - val_acc: 0.8540\nEpoch 10/30\n8000/8000 [==============================] - 0s 21us/step - loss: 0.3550 - acc: 0.8535 - val_loss: 0.3678 - val_acc: 0.8540\nEpoch 11/30\n8000/8000 [==============================] - 0s 22us/step - loss: 0.3534 - acc: 0.8529 - val_loss: 0.3667 - val_acc: 0.8530\nEpoch 12/30\n8000/8000 [==============================] - 0s 21us/step - loss: 0.3518 - acc: 0.8549 - val_loss: 0.3666 - val_acc: 0.8555\nEpoch 13/30\n8000/8000 [==============================] - 0s 21us/step - loss: 0.3506 - acc: 0.8536 - val_loss: 0.3658 - val_acc: 0.8565\nEpoch 14/30\n8000/8000 [==============================] - 0s 21us/step - loss: 0.3493 - acc: 0.8559 - val_loss: 0.3638 - val_acc: 0.8560\nEpoch 15/30\n8000/8000 [==============================] - 0s 22us/step - loss: 0.3482 - acc: 0.8544 - val_loss: 0.3642 - val_acc: 0.8565\nEpoch 16/30\n8000/8000 [==============================] - 0s 22us/step - loss: 0.3474 - acc: 0.8568 - val_loss: 0.3611 - val_acc: 0.8550\nEpoch 17/30\n8000/8000 [==============================] - 0s 24us/step - loss: 0.3466 - acc: 0.8575 - val_loss: 0.3622 - val_acc: 0.8560\nEpoch 18/30\n8000/8000 [==============================] - 0s 22us/step - loss: 0.3457 - acc: 0.8580 - val_loss: 0.3589 - val_acc: 0.8560\nEpoch 19/30\n8000/8000 [==============================] - 0s 24us/step - loss: 0.3450 - acc: 0.8569 - val_loss: 0.3584 - val_acc: 0.8580\nEpoch 20/30\n8000/8000 [==============================] - 0s 24us/step - loss: 0.3440 - acc: 0.8583 - val_loss: 0.3580 - val_acc: 0.8560\nEpoch 21/30\n8000/8000 [==============================] - 0s 22us/step - loss: 0.3433 - acc: 0.8585 - val_loss: 0.3582 - val_acc: 0.8570\nEpoch 22/30\n8000/8000 [==============================] - 0s 22us/step - loss: 0.3428 - acc: 0.8596 - val_loss: 0.3572 - val_acc: 0.8565\nEpoch 23/30\n8000/8000 [==============================] - 0s 21us/step - loss: 0.3421 - acc: 0.8604 - val_loss: 0.3560 - val_acc: 0.8595\nEpoch 24/30\n8000/8000 [==============================] - 0s 21us/step - loss: 0.3418 - acc: 0.8580 - val_loss: 0.3556 - val_acc: 0.8585\nEpoch 25/30\n8000/8000 [==============================] - 0s 21us/step - loss: 0.3409 - acc: 0.8587 - val_loss: 0.3552 - val_acc: 0.8585\nEpoch 26/30\n8000/8000 [==============================] - 0s 21us/step - loss: 0.3409 - acc: 0.8604 - val_loss: 0.3556 - val_acc: 0.8580\nEpoch 27/30\n8000/8000 [==============================] - 0s 21us/step - loss: 0.3402 - acc: 0.8597 - val_loss: 0.3553 - val_acc: 0.8580\nEpoch 28/30\n8000/8000 [==============================] - 0s 21us/step - loss: 0.3397 - acc: 0.8605 - val_loss: 0.3555 - val_acc: 0.8580\nEpoch 29/30\n8000/8000 [==============================] - 0s 21us/step - loss: 0.3394 - acc: 0.8605 - val_loss: 0.3535 - val_acc: 0.8590\nEpoch 30/30\n8000/8000 [==============================] - 0s 21us/step - loss: 0.3390 - acc: 0.8594 - val_loss: 0.3533 - val_acc: 0.8615\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"show_history(history)","execution_count":82,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 864x288 with 2 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3PGeM+u0AcqWLUtGRkbcY1esWHGfbdw9X/HltH2dOnWYO3cuXbp04aGHHuKKK64AYPLkyQwdOpTPPvuMtLQ0du3ala/zFXH1gRUx71dGy2I9AjQFvgXmAde6+27gCGANMNbMZpnZk2ZWlTjMbIiZzTCzGWvWrCnwDyFSGiXaQlzYvwMSPV8i8Sda0htvO4CyZeGNN+Daa+H555NbHqPkWqQUSWV/g02bNlG9enVq1KjB6tWrmTx5coGfo0uXLkyYMAGAefPmxW0Zj9WpUyfef/991q1bR0ZGBuPHj6dr166sWbMGd+dXv/oVf/7zn/n888/ZtWsXK1eupHv37owaNYo1a9awLftvg+It3gDc2f/6OAOYDRwGtAEeiVqtywFtgcfc/XhgK7BPzTaAu49x9zR3T6tbt26BBS9SmiXaQjxiBMS0XwBQqRLcddf+nTevUo5Ef+ckEn+iJb2x22WqWxdmzIDopmjSy2OUXIuUIqnsb9C2bVuaNWtGixYtuPzyy+ncuXOBn+Oaa65h1apVtGrVivvvv58WLVrsKemIp0GDBtxxxx1069aNNm3a0KlTJ84++2xWrFjBSSedRJs2bbj88ssZOXIkGRkZXHzxxbRq1Yq2bdty4403Ur169QL/DCm0Ejg85n0DQgt1rMHAKx4sApYAx0X7rnT3adF2LxGSbRE5QInUUifaQrxjB+zcCdGNPwC2b4eRI+G222D+/PzFlVfrb6K/cxKNP9GS3hNOgIsuCq+7dAmfK7ZKMOnlMe5eYh7t2rVzkdJmwYIFqQ6hyNi5c6f/9NNP7u7+v//9zxs3buw7d+5McVT7ivczA2Z4Cq+fhNbnxUAToAIwB2iebZvHgNuj14cAq4CDo/dTgWOj17cDo/I6p67ZUhyNG+feqJG7WXgeNy55xxo3zr1KFfeQvoZHlSr7t93YseE8Z5zhHl0m/bvv3P/2N/du3cI6cG/WzP2CC9wPPTT3z9ioUdbzZT4aNdq/7yGRz5mbxYvd77nHvV27vce49FL37duTE3tu1+yUJ8QF+dCFWkojJdd7/fjjj962bVtv1aqVt2zZ0idPnpzqkOIqisl1CIGzgP8B3wDDo2VDgaHR68OAfxPqrb8ABsTs2waYAcwFJgK18zqfrtlS3BREEpifY+UnCcwtUX/66bD89NP3JtbZrV7t/uij7scdF/+c1aq516mz9xFvm8zH0qXuu3fn//vI7x8tS5e6jxrl3r793nO3bx+WLVmS+7kO9OeY2zXbwvqSIS0tzWfMmJHqMEQK1cKFC2natGmqw5B8iPczM7OZ7p6WopBSQtdsKUrS00PN7fLloRxhxIh9yw4aNw7lD9k1ahTKFGJt2xbqfE88MZREZJfIscqUCalfdmaJT9n9zDMweDCceiq89hpUrpz79jnFVb16GB8607PPwubNOR+nWjVo1iyUYzRvHr7TMgVUjLxkCbz4Inz2WXiflga/+lV4NGmS2DES+XnnJrdrdrnEDyMiIiJS8mTWD2d2csusH4asCVeitboffxwS2q+/Dkntk09m7WCX6LEaNoyf6CY6usezz4Y4TjklscQ6t7i2bIHY4f1POCHrdwbh+DfcAPXrhzrn+fPhrbdg7NjE4s2Pdu3g7rtDQn3EEfnfv3//5PU3SmpybWZnEiYWKAs86e5357Bde+C/QB93fylaVgt4EmhB6LH+G3f/NJnxioiISMmTVytlbqNHxG6XV7L7009w883wwAN7W2nffReOOw4eegguu2xvK3YiifOIEfsmsImO8DRuHAwaFKb5TjSxTjQu2Pu9JNL6u24dfJu9e/QBqFULDj887+1SJqd6kQN9EBLqbwjjn2Z2jmmWw3bvAW8BF8Usfwa4LHpdAaiV1zlVvyelkWqui5+iWnNd2A9ds6UwJFJfm9mZL/vDLPFjffKJ+zHHhGXlymXdpkyZ8Hz66e7LlyceV+Z2DRuG9Q0bJlYXPG5cOGf37u5btxb89yW5X7OTORRfIrN9AVwDvAz8kLlAs32JiIhIQUhkTOP8DAWXfWi5Rx6BOXPCkG/bt8MvfgHZ577avTtMtf3xx9CiBfzjH3DxxbkPU+cOc+fCwoVhLGqAH36A+++HgQNDScTrr8PixVnrr59/PtRGd+sW1sebUCU3qRyytaRIZnKd52xfZlYfOB94PNu+mu1LpJjo1q3bPhPCjB49miuvvDLX/apVqwbAt99+y0WZA5LGOXZeHd5Gjx6dZTKXs846iw0bDvxv8dtvv5377rvvgI8jIllt2hRKFkaOhFdega++2jcZhdzHd3aH776D996Dhx8OJRebNsU/XyK1zfmZYCt2rOV//hPuvRdGjQolH/PmQU6pyI8/hmT5+OPDtmedBV27Zh23+eKLwzFuvRWaNoXWreEvfwklEPfeC7/9bUjeP/gA/vQnOO88OPLI0HmwXbtQfzxwYDju/iTW8T5jbuNJS3zJrLlOZLav0cCN7r7LsnalzZzt6xp3n2ZmDxJm+7plnwO6jwHGQOh5XhCBi0ji+vXrx/jx4znjjDP2LBs/fjyjRo1KaP/DDjuMl156ab/PP3r0aAYMGECV6LfIW2+9td/HEpHk2LQJJk0KIzy8/XaYzCRWhQqhLjlzZIm1a+Hxx0NLMIQa4MGDQ8e4HTtCR7n167Me4/bb4Q9/gGuugRo19i5PpIY4P/XDAD//HM53772h897kyXD66Xmf74gjwh8Ef/sb3HhjaMUePTqMdvHiizBhAnz5ZfiDols3uO46OP/8kFBnt3EjLFiwt+Pg/Pnw6adw7rnhD5H9TaylAORUL3KgD+AEYHLM+z8Bf8q2zRJgafTYQigN6QXUA5bGbHci8GZe51T9npRGqa65Xrt2rR988MG+PRqpf8mSJX744Yf77t27ffPmzd69e3c//vjjvUWLFj5x4sQ9+1WtWnXP9s2bN3d3923btnmfPn28ZcuW3rt3b+/QoYNPnz7d3d2HDh3q7dq182bNmvmtt97q7u4PPvigly9f3lu0aOHdunVzd/dGjRr5mjVr3N39/vvv9+bNm3vz5s39gQce2HO+4447zi+77DJv1qyZn3baab5t27Z9Ptdtt93mo0aNcnf3WbNmeceOHb1ly5beq1cvX79+/Z7zN23a1Fu2bOl9+vRxd/cPPvjAW7du7a1bt/Y2bdr4pk2b9jm2aq51zS4NNm4Mdbo9e7pXrBhqdxs0cL/uulCfvGmT+2efhclN/vhH97POynlc59ja5c6d3S+/3H30aPd33nH/9lv3GTPczz03bHPQQe4jRoTjuydWQ7x+fYijRw/32rXda9XK/ZF5vMsuc9+wIevnTrRmedEi9xNPzPrZTj45TOry3XdJ/MFIgcjtmp3MluvpwNFm1oQwi1df4OJsif2e0QjN7GngDXefGL1fYWbHuvtXwCnAgiTGKlIi/P73MHt2wR6zTZvQspKTOnXq0KFDB95++2169uzJ+PHj6dOnD2ZGpUqVePXVV6lRowZr166lU6dOnHfeeVi8QV+Bxx57jCpVqjB37lzmzp1L27Z7Z9AeMWIEBx10ELt27eKUU05h7ty5/O53v+Ovf/0r77//PgcffHCWY82cOZOxY8cybdo03J2OHTvStWtXateuzddff80LL7zAE088Qe/evXn55ZcZMGBAjp/xkksu4eGHH6Zr167ceuut/PnPf2b06NHcfffdLFmyhIoVK+4pRbnvvvt49NFH6dy5M1u2bKFSZrGkSCkwblxobV27du+y+vVDOcOvfgWdOmUd67h9+/CItXlz1pbnWLt3w0cf7bv80ENDy/iMGaFFefjwUJt8ww1w9dVhm+yt0mefHcaAnjAB3nknTAveqFGIM5H/tuecA6edtu/yRFvBjzwylHeMGwdbt4YW6nr18j6vFH1JS67dPcPMrgYmE0YEecrd55vZ0Gh99jrr7K4B0s2sAmFK3sHJilVEDkxmaUhmcv3UU08B4c7YTTfdxJQpUyhTpgyrVq3i+++/p14Ov0GmTJnC7373OwBatWpFq1at9qybMGECY8aMISMjg9WrV7NgwYIs67P76KOPOP/886laNXTXuOCCC5g6dSrnnXceTZo0oU2bNgC0a9eOpdlnf4ixceNGNmzYQNeuXQH49a9/za9+9as9Mfbv359evXrRq1cvADp37sz1119P//79ueCCC2jQoEEiX6FIsTd2LFx+OezatXdZxYqhZnjgwMSPU716SHJzmmAlN2lp8MYbMH16SLJvuikk2X/8I3zxRYjttdfghRdCmcnOnSH5vfbakFS3bx9/wpf8SnQM5TJlsk7MIiVDUse5dve3CEPsxS6Lm1S7+6Bs72cDpWq2MpEDlVsLczL16tWL66+/ns8//5yffvppT4tzeno6a9asYebMmZQvX57GjRuzPbOIMgfxWrWXLFnCfffdx/Tp06lduzaDBg3K8zjhrl18FStW3PO6bNmy/PTTT7keKydvvvkmU6ZMYdKkSdx5553Mnz+fYcOGcfbZZ/PWW2/RqVMn/vOf/3Dcccft1/FFiovvvoOhQ7Mm1hBqk2+5JX/JNSQ+vnNO41e3bw9vvgnTpsGf/wzDhoXRNbZtCzXbDRvC734HvXsXXEItkimZo4WISClRrVo1unXrxm9+8xv69eu3Z/nGjRv5xS9+Qfny5Xn//fdZFq8pKsZJJ51EejQkwBdffMHcuXMB2LRpE1WrVqVmzZp8//33/Otf/9qzT/Xq1dkcZw7ek046iYkTJ7Jt2za2bt3Kq6++yoknnpjvz1azZk1q167N1KlTAXjuuefo2rUru3fvZsWKFZx88snce++9bNiwgS1btvDNN9/QsmVLbrzxRtLS0vjyyy/zfU6RvLiHSTmyJ7OpMHs2dOiwbyfFTDmN1pGbRIaDy5xVcdmy8H1kzqoYO6pIx45hhsBPP4UePUKJyH//G0bAuO++ELcSaylomv5cRApEv379uOCCCxg/fvyeZf379+fcc88lLS2NNm3a5NmC+9vf/pbBgwfTqlUr2rRpQ4cOHQBo3bo1xx9/PM2bN+eII46gc+fOe/YZMmQIPXr04NBDD+X999/fs7xt27YMGjRozzEuu+wyjj/++FxLQHLyzDPPMHToULZt28YRRxzB2LFj2bVrFwMGDGDjxo24O9dddx21atXilltu4f3336ds2bI0a9aMHj165Pt8IvG4h5rizFElli0LZRexo2xkPpo0gbJlkx/TxIkh4T3ooFD3vHr1vttkHys6r9kSM+VVWpHorIoQar07dUrsM4kcKMvt1mlxk5aW5nmNiStS0ixcuJCmTZumOgzJh3g/MzOb6e6lqhRO1+y8ucPMmXsT6qVLoVy50JHutNNg1aq9w7CtiJlZolKlvUn3IYfk3jq7cGHoJLhpU0h2R47Mu17YPQxD96c/hbKKiRPDEHPxSjliW5wzW5tz2yZRZcqEOLIzyzqpikgy5HbNVsu1iIhIEeIOs2aFZHrCBFiyJCTUp54aJhfp2TO0FGe3aVNIlGPHPf7wwzB5SU4yMkJddKbly2HQoDB+8h13xG/9/vlnuOKKMNJGnz6hI2PlyomNkpGf1ua8JDJ+tUgqKLkWEREpImbNgksvDc/lysEpp8DNN0OvXvET6lg1aoQa444dEz9f48b7JqgZGaH1+skn4cILQ6e/FStCx8TMUpSffw4dBW+5JWureF6lHInMlpioRDs9ihQ2JdciJYC75zh2tBQtJakUTwrOjh0hKRw5EurWhb//PSS2deok97y5JbXduoXW6ccey7r855/DjIpHHpn/zoAF2dqc31kVRQqLRgsRKeYqVarEunXrlLQVA+7OunXrNLGMZDFnThi14o47oG/fMB7zkCHJT6wh56S2USP45z/hhx8g2/xMQPhjYPjw/J9vxIh9p+U+kNbm/v1DHfru3eFZibUUBWq5FinmGjRowMqVK1mzZk2qQ5EEVKpUSRPLCBAmMBk5Eu66KyTSEyeGeurClFdpRdWqsG5d/H33d4g9UGuzlGxKrkWKufLly9OkSZNUhyEi+TB3bug4OGsWXHwxPPRQwbdUJzLkXSLJbkF3HEx09kKR4kplISIiIoVk5064884wTfeqVfDKKyEJTkZindcEK5nyKq0o6FIOkZJOybWIiEghWLo0TGRy665v0dkAACAASURBVK2hs+L8+XD++fk/Tnp6GOWjTJnwHC9hzm3Iu/xKZLZEEdlLZSEiIiJJ9uOPYfrt774LE8JcdNH+HSf7JCyZLdKQNdktyCHvMo+tZFokMWq5FhERSaKdO0My/c03odPi/ibWkHiLdE710JpgRST5lFyLiAgAZnammX1lZovMbFic9TXN7HUzm2Nm881scMy6pWY2z8xmm5nmNI+4w29/G6YGf+IJ6No19+3zKvlItEVaddIiqaPkWkREMLOywKNAD6AZ0M/MmmXb7Cpggbu3BroB95tZhZj1J7t7G3dPK4yYi4NRo+Af/wgty7/+de7bJtIJMdEWadVJi6SOkmsREQHoACxy98XuvgMYD2QfddmB6hamA60GrAcyCjfM4uPll+HGG6FPnzBBTF4SKfnIT4u0JlgRSQ0l1yIiAlAfWBHzfmW0LNYjQFPgW2AecK27747WOfBvM5tpZkNyOomZDTGzGWY2oyRPfPTZZzBgQBgdZOzYUOaRl0RKPtQiLVL0KbkWEREAi7PMs70/A5gNHAa0AR4xsxrRus7u3pZQVnKVmZ0U7yTuPsbd09w9rW7dugUUetGyfDmcdx7UqwevvQaVKye2X35KPtQiLVJ0KbkWEREILdWHx7xvQGihjjUYeMWDRcAS4DgAd/82ev4BeJVQZlLqbNoEZ58NP/0Eb74Jv/hF4vuqE6JIyaDkWkREAKYDR5tZk6iTYl9gUrZtlgOnAJjZIcCxwGIzq2pm1aPlVYHTgS8KLfIiIiMj1FcvXAgvvQTNsncHzYNKPkRKBk0iIyIiuHuGmV0NTAbKAk+5+3wzGxqtfxy4E3jazOYRykhudPe1ZnYE8Gro50g54Hl3fzslHyRF3OHaa+Htt0NCfNpp+3ccTdYiUvwlteU6rzFTY7Zrb2a7zOyibMvLmtksM3sjmXGKiAi4+1vufoy7H+nuI6Jlj0eJNe7+rbuf7u4t3b2Fu4+Lli9299bRo3nmvqXFTz/ByJHwt7/BDTfA5Zfvu00iU5aLSMmQtJbrmDFTTyPU8k03s0nuviDOdvcQWkuyuxZYCNSIs05ERCQltm8PrdQTJsDrr8OWLXDBBXDPPftum+iU5SJSMiSz5TqRMVMBrgFeBn6IXWhmDYCzgSeTGKOIiEhCtm8Po3/07w9168L558O//w39+sE774REO96Qe4lOWS4iJUMya67jjZnaMXYDM6sPnA90B9pn23808H9A9dxOEo2nOgSgYU7jGImIiOyH3bvhjTdC4jxpEmzeDHXqQN++0Ls3dOsG5cvnfoxEpywXkZIhmcl1ImOmjiZ0iNkVdYQJO5qdA/zg7jPNrFtuJ3H3McAYgLS0tOzHFxER2W9/+QvcfDPUrh2S6d694eST806oYzVsGEpB4i0XkZInmcl1ImOmpgHjo8T6YOAsM8sgtHCfZ2ZnAZWAGmY2zt0HJDFeERGRPXbvDiN/dO8e6qvzk1DHGjEia801aPxqkZIsmTXXeY6Z6u5N3L2xuzcGXgKudPeJ7v4nd28QLe8LvKfEWkRECtOHH4bSjcsu2//EGjR+tUhpk7SW6wTHTBURESmSnn4aatSAXr0O/Fgav1qk9EjqJDLu/hbwVrZlcZNqdx+Uw/IPgA8KODQREZEcbd4cZlns3x8qV051NCJSnGj6cxERkWxefjnUSA8alOpIRKS4UXItIiKSzdNPw9FHwwknpDoSESlulFyLiIjEWLIkdGb89a9DB0QRkfxQci0iIhLj2WdDUj1wYKojEZHiSMm1iIhIZPdueOaZMLZ1opO8pKdD48Zh6vPGjcN7ESm9lFyLiIhEPvoolIUk2pExPT1MELNsGbiH5yFDlGCLlGZKrkVERCLPPAPVqsH55ye2/fDhWWdehPB++PCCj01Eigcl1yIiIsDWrTBhAvTuDVWrJrbP8uX5Wy4iJZ+SaxEREeDVV2HLlvyNbZ1TXXai9doiUvKU+uR65cpURyAiIkXB00/DEUdAly6J7zNiBFSpknVZlSphuYiUTqU6uX7mGTj8cPjmm1RHIiIiqbR8Obz3Xv7Htu7fH8aMgUaNwn6NGoX3/fsnL1YRKdpKdXJ9yinhYjhuXKojERGRVHruuTDaxyWX5H/f/v1h6dIwjN/SpUqsRUq7Up1cN2gAJ58ckmv3VEcjIiKp4B7uZHbrFsapFhE5EKU6uQYYMAAWLYJp01IdiYiIpMKnn8LXX4eSEBGRA1Xqk+sLL4RKlcItQRERKX2efjoMvXfRRamORERKglKfXNeoAT17wj//CTt2pDoaEZHUMbMzzewrM1tkZsPirK9pZq+b2Rwzm29mg7OtL2tms8zsjcKL+sD89FO4/l90UZg8RkTkQJX65Bpg4EBYtw7efjvVkYiIpIaZlQUeBXoAzYB+ZtYs22ZXAQvcvTXQDbjfzCrErL8WWFgI4RaYiRNh0yaVhIhIwVFyDZx+OtStq1FDRKRU6wAscvfF7r4DGA/0zLaNA9XNzIBqwHogA8DMGgBnA08WXsgH7plnwvB5XbumOhIRKSmUXAPly0PfvjBpEmzYkOpoRERSoj6wIub9ymhZrEeApsC3wDzgWnffHa0bDfwfsJtcmNkQM5thZjPWrFlTIIHvr1Wr4J13wvB7ZfTbUEQKiC4nkYED4eef4eWXUx2JiEhKxJs6JfsgpWcAs4HDgDbAI2ZWw8zOAX5w95l5ncTdx7h7mrun1a1b94CDPhDjxoWxqXMqCUlPD0PzlSkTntPTCzM6ESmulFxH0tLgmGM0aoiIlForgcNj3jcgtFDHGgy84sEiYAlwHNAZOM/MlhLKSbqbWZEutHMPo4SceCIceeS+69PTYcgQWLYsbLtsWXivBFtE8pLU5Dqvnucx27U3s11mdlH0/nAze9/MFkY90q9NZpzhnKH1+sMPwzS4IiKlzHTgaDNrEnVS7AtMyrbNcuAUADM7BDgWWOzuf3L3Bu7eONrvPXcfUHih59/ixfDll9CnT/z1w4fDtm1Zl23bFpaLiOQmacl1gj3PM7e7B5gcszgD+IO7NwU6AVfF27egZU5ZG9syoduCIlIauHsGcDXhWrwQmODu881sqJkNjTa7E/ilmc0D3gVudPe1qYn4wEydGp67dYu/PqdGFjW+iEheyiXx2Ht6ngOYWWbP8wXZtrsGeBlon7nA3VcDq6PXm81sIaFjTfZ9C1STJtClSygNGTYMnn8+3AbMbL3IvC0IexNxEZGSwt3fAt7KtuzxmNffAqfncYwPgA+SEF6BmjoVDjoImjaNv75hw3DNj7dcRCQ3ySwLybPnuZnVB84HHicHZtYYOB6IO0F5Qfc8HzAAFi6EWbN0W1BEpKSaOjU0puQ0SsiIEVClStZlVaqE5SIiuUlmcp1Iz/PRhNuKu+IewKwaoVX79+6+Kd42Bd3zvHdvqFAhtF7rtqCISMnz3Xfw9dehM2NO+veHMWPCGNhm4XnMGN21FJG8JbMsJJGe52nA+DAfAQcDZ5lZhrtPNLPyhMQ63d1fSWKcWdSuDWefDS+8AIcfHj+R1m1BEZHiK7PeOrfkGkIirWRaRPIrmS3XefY8d/cm7t446mH+EnBllFgb8A9gobv/NYkxxjVwIHz/fehFrtuCIiIly9Sp4Vretm2qIxGRkihpyXWCPc9z0hkYSBgrdXb0OCtZsWZ31lmhBfvbb3VbUESKHzO72sxqpzqOomrqVDjhhDA7r4hIQUtmWUiePc+zLR8U8/oj4tdsF4qKFUPt9XPPweOPK5kWkWKnHjDdzD4HngImu3v2Pi+l0saNMGcO3HZbqiMRkZJKMzTmYODAMDLIK4VW7S0iUjDc/WbgaEJ53SDgazMbaWZx5iIsXT75JMy4mFe9tYjI/lJynYNf/jKMez2uSE/gKyISX9RS/V30yABqAy+Z2b0pDSzFpk6FcuWgU6dURyIiJZWS6xyYhTGv33031F6LiBQXZvY7M5sJ3At8DLR0998C7YALUxpcik2ZAu3a7dtZXUSkoCi5zsWAAbB7dxiWT0SkGDkYuMDdz3D3F919J4C77wbOSW1oqbN9O0yfrpIQEUkuJde5OOYY6NAhdGwUESlG3gLWZ74xs+pm1hHA3RemLKoU++wz2LFDybWIJJeS6zwMGBB6ls+bl+pIREQS9hiwJeb91mhZqZY5eUyXLqmNQ0RKNiXXeejbN3R+Ueu1iBQjFjv0XlQOktShV4uDqVOhRQs46KBURyIiJZmS6zzUrQvnnAP/+EcYmk9EpBhYHHVqLB89rgUWpzqoVMrICMPwqSRERJJNyXUC/vAHWL8enn461ZGIiCRkKPBLYBWwEugIDElpRCk2Zw5s3qzkWkSST8l1Ajp3ho4d4a9/hV27Uh2NiEju3P0Hd+/r7r9w90Pc/WJ3/yHVcaVSZr21kmsRSbaEkmszO9LMKkavu0W3G2slN7SiwwxuuAG++QZeey3V0YiI5M7MKpnZVWb2NzN7KvOR6rhSaepUaNwYGjRIdSQiUtIl2nL9MrDLzI4iTKfbBHg+aVEVQeefH2ZsvO++VEciIpKn54B6wBnAh0ADYHNKI0oh95Bcq9VaRApDosn1bnfPAM4HRrv7dcChyQur6ClbFq6/Hj79NHSKEREpwo5y91uAre7+DHA20DLFMaXM//4Ha9bASSelOhIRKQ0STa53mlk/4NfAG9Gy8skJqegaPBhq14b77091JCIiudoZPW8wsxZATaBx6sJJrSlTwrNarkWkMCSaXA8GTgBGuPsSM2sCjEteWEVT1apw5ZXw6quwaFGqoxERydEYM6sN3AxMAhYA96Q2pNSZOhV+8Ysw626m9PRQg12mTHhOT09VdCJS0iSUXLv7Anf/nbu/EF2wq7v73UmOrUi6+mooXx4eeCDVkYiI7MvMygCb3P1Hd5/i7kdEo4b8PdWxpcrUqWFWRrPwPj0dhgyBZctCPfayZeG9EmwRKQiJjhbygZnVMLODgDnAWDP7a3JDK5rq1YOBA2HsWFi7NtXRiIhkFc3GeHWq4ygqVq6EpUuzloQMH77vpGDbtoXlIiIHKtGykJruvgm4ABjr7u2AU5MXVtF2/fXw00/w2GOpjkREJK53zOwGMzvczA7KfOS1k5mdaWZfmdkiMxsWZ31NM3vdzOaY2XwzGxwtr2Rmn8Us/3MyPtT+iDe+9fLl8bfNabmISH4kmlyXM7NDgd7s7dBYajVrBmefDQ8/DNu3pzoaEZF9/Aa4CpgCzIweM3LbwczKAo8CPYBmQD8za5Zts6uABe7eGugG3G9mFYCfge7R8jbAmWbWqeA+zv6bOhWqVYPWrfcua9gw/rY5LRcRyY9Ek+s7gMnAN+4+3cyOAL5OXlhF3w03hKGdnnsu1ZGIiGTl7k3iPI7IY7cOwCJ3X+zuO4DxQM/shwaqm5kB1YD1QIYHW6JtykcPL7hPtP+mTAmz7JYrt3fZiBFQpUrW7apUCctFRA5Uubw3AXd/EXgx5v1i4MJkBVUcdO0K7dqFYfkuvTT0OBcRKQrM7JJ4y9392Vx2qw+siHm/EuiYbZtHCKOPfAtUB/pENd6ZLd8zgaOAR919Wg6xDQGGADRMclPxunUwfz7065d1ef/+4Xn48FAK0rBhSKwzl4uIHIhEOzQ2MLNXzewHM/vezF42s1I9iWzmlOhffQVvvpnqaEREsmgf8zgRuB04L499LM6y7K3PZwCzgcMI5R+PmFkNAHff5e5tCLNBdojG1973gO5j3D3N3dPq1q2b4MfZPx9/HJ7jjW/dv3/o6Lh7d3hWYi0iBSXR9taxhNaKwwitG69Hy3KVV+eYmO3am9kuM7sov/um0kUXhRYPTYkuIkWJu18T87gcOB6okMduK4HDY943ILRQxxoMvBKVgSwClgDHZTv3BuAD4MwD+AgFYupUqFABOnRIdSQiUpokmlzXdfex7p4RPZ4Gcm1ySLBzTOZ29xBquvO1b6qVKwfXXRdq+g49VJMRiEiRtQ04Oo9tpgNHm1mTqJNiX0KjSqzlwCkAZnYIcCyw2MzqmlmtaHllwmhSXxZg/Ptl6lRo3x4qVUp1JCJSmiRUcw2sNbMBwAvR+37Aujz22dM5BsDMMjvHLMi23TXAy4Tbl/ndN+WqVQvP330XnjMnIwDdZhSR1DCz19lb0lGG0EgxIbd93D3DzK4mNHSUBZ5y9/lmNjRa/zhwJ/C0mc0jlJHc6O5rzawV8EzUMFIGmODuKR1ZautWmDkzlO+JiBSmRJPr3xA6sjxAuGB/Qrg9mJs8O8eYWX3gfKA7WZPrRDrWZB6j0DrHxHPXXfsuy5yMQMm1iKRIbLFaBrDM3VfmtZO7vwW8lW3Z4zGvvwVOj7PfXELpSZHx3/9CRkb8emsRkWRKdLSQ5WTrDGNmvwdG57JbIp1jRhNaPnaZZdk8kX0zYxsDjAFIS0sr9KGfNBmBiBRBy4HV7r4dQqmGmTV296WpDavwTJ0aOp537pzqSESktDmQAeSuz2N9Ip1j0oDxZrYUuAj4m5n1SnDfIkGTEYhIEfQisDvm/S5ihlMtDaZODRPH1KyZ6khEpLQ5kOQ6XutyrDw7x0QTGzR298bAS8CV7j4xkX2LiniTEVSurMkIRCSlykUTwQAQvc5rtJASY+fOUBaikhARSYUDSa5zLcFw9wwgs3PMQkIHl/lmNjSzg0x+9z2AWJOmf38YMwYaNdq7rF8/1VuLSEqtMbM9pXxm1hNYm8J4CtXnn4e+L0quRSQVcq25NrPNxE+iDaic18Hz6hyTbfmgvPYtqvr3D4/du6FHD3j+ebj+emjePNWRiUgpNRRIN7NHovcrgbizNpZEU6aEZyXXIpIKuSbX7l69sAIpCcqUgWeeCXV+/frBtGmhREREpDC5+zdAJzOrBpi7b051TIVp/PhwHa5XL9WRiEhpdCBlIRJHvXrw9NMwbx788Y+pjkZESiMzG2lmtdx9i7tvNrPaZhZn4NCSZ+bMUBZy+eWpjkRESisl10nQo0coC3n0UXjttVRHIyKlUI9oGnIA3P1H4KwUxlNonngi3DFUvxcRSRUl10kyciS0bQu/+Q2szHPqBhGRAlXWzCpmvommJK+Yy/YlwpYtkJ4OffpArVqpjkZESisl10lSsSK88AL8/DMMGAC7dqU6IhEpRcYB75rZpWZ2KfAO8EyKY0q68eNDgj1kSKojEZHSTMl1Eh1zTCgN+fBDuPvuVEcjIqWFu98L3AU0BZoBbwONct2pBBgzBlq0gE6dUh2JiJRmSq6T7JJL4OKL4bbb4JNPUh2NiJQi3xFmabwQOIUwZ0CJNWsWTJ8eWq0trynORESSSMl1kpnBY4+F6dAvvhg2bMh7HxGR/WFmx5jZrWa2EHgEWEEYiu9kd38kj92LtSeegEqVQhmeiEgqKbkuBDVqhPrrVatCq4rnOreliMh++5LQSn2uu3dx94eBEt/jY+tWGDcOeveG2rVTHY2IlHZKrgtJx45w553w4ouhhUVEJAkuJJSDvG9mT5jZKYQZdUu0f/4TNm/W2NYiUjQouS5E//d/cPrpMHRo6OgoIlKQ3P1Vd+8DHAd8AFwHHGJmj5nZ6SkNLonGjIGmTaFz51RHIiKi5LpQlSkDr74K550HV18dku3du1MdlYiUNO6+1d3T3f0coAEwGxiW4rCSYs4cmDZNHRlFpOhQcl3IqlSBl1+GK6+EUaPCLGI//5zqqESkpHL39e7+d3fvnupYkuGJJ8K8AgMHpjoSEZFAyXUhSk+Hxo2hfHl44w3o2zdMenDGGRpFREQkv7Ztg+eeg4sugjp1Uh2NiEig5LqQpKeH25bLloXRQpYvh0mTQgv2J5+EWsHly1MdpYhI8TFhAmzapBkZRaRoUXJdSIYPD60ssbZtgzffhMmTwzB9nTrB7NmpiU9EpLgZMwaOPRZOPDHVkYiI7KXkupDk1Cq9fDmcfDJ89BGULQsnnQTvvFO4sYmIFDdffAGffqqOjCJS9Ci5LiQNG+a+vEUL+O9/oUkTOOsseOaZwotNRKS4eeIJqFABLrkk1ZGIiGSl5LqQjBgRRgqJVaVKWJ6pfn2YMgW6doVBg+CuuzSbo4gUHjM708y+MrNFZrbP0H1mVtPMXjezOWY238wGR8sPN7P3zWxhtPzaZMb500/w7LNw4YVw8MHJPJOISP4puS4k/fuH+sBGjcItzEaNwvv+/bNuV7MmvPUWDBgAt9wCV10Fu0r85MUikmpmVhZ4FOgBNAP6mVmzbJtdBSxw99ZAN+B+M6sAZAB/cPemQCfgqjj7FpiXXgojLMXryJg5KlOZMuE5PT1ZUYiIxFcu1QGUJv3775tMx1OhQigLqV8f7rkHVq+G55+HypWTH6OIlFodgEXuvhjAzMYDPYEFMds4UN3MDKgGrAcy3H01sBrA3Teb2UKgfrZ9C8yYMXD00eEuX6zMUZkyO48vW7Y3AU/k2isiUhCS2nKdwC3GnmY218xmm9kMM+sSs+666PbiF2b2gplVSmasRU2ZMnD33fDQQ/Daa3DqqbBuXaqjEpESrD6wIub9ymhZrEeApsC3wDzgWnfPMs+smTUGjgemxTuJmQ2Jrvcz1qxZk+8gFywIHcDjdWTMaVSm4cPzfRoRkf2WtOQ6wVuM7wKt3b0N8BvgyWjf+sDvgDR3bwGUBfomK9ai7JprwliuM2dCly6hJUZEJAnijbmRvdfHGYSp1A8D2gCPmFmNPQcwqwa8DPze3TfFO4m7j3H3NHdPq1u3br6DfOKJMBHXr3+977rcRmUSESksyWy53nOL0d13AJm3GPdw9y3ue7rsVSXrhbwcUNnMygFVCC0lpdJFF8G//w3ffQcnnKCxsEUkKVYCh8e8b8C+193BwCseLAKWAMcBmFl5QmKd7u6vJCPA7dtDydz550O8vDyvUZlERApDMpPrRG4xYmbnm9mXwJuE1mvcfRVwH7CcUMe30d3/He8kB3qLsbg46aSsY2G/+26qIxKREmY6cLSZNYk6KfYFJmXbZjlwCoCZHQIcCyyOarD/ASx0978mK8DVq6FZs5xnZExkVCYRkWRLZnKdyC1G3P1Vdz8O6AXcCWBmtQmt3E0Itx+rmtmAeCc50FuMxUnz5mHShEaNoEeP0MlRRKQguHsGcDUwGVgITHD3+WY21MyGRpvdCfzSzOYRyvpudPe1QGdgINA96kMz28zOKugYmzQJjQynnBJ/faKjMomIJFMyRwtJ5BbjHu4+xcyONLODgZOBJe6+BsDMXgF+CYxLYrzFQoMGMHUq9OoVfmGsWAH/93+aoUxEDpy7vwW8lW3Z4zGvvwVOj7PfR8RvUCl0iY7KJCKSLMlsuc7zFqOZHRXdTsTM2gIVgHWEW4+dzKxKtP4UQktKqZDXOK21asHkydCnDwwbFhLt9etTEamIiIiIxEpacp3gLcYLgS/MbDZhZJE+UUeZacBLwOeE4Z7KAGOSFWtRkjlO67JlYXbGzHFasyfYFSvCCy/A6NHwr3/B8ceHkhERERERSR3zEjS/dlpams+YMSPVYRyQxo3jD7fXqBEsXRp/n+nTQyv2ihUwciT84Q+h1VtEig8zm+nuaamOozCVhGu2iJROuV2zlYIVMfszTmv79vD559CzZ6i/PvdcWLs2OfGJiIiISM6UXBcx+ztOa61a8OKL8Mgj8J//QJs2oVe9iIiIiBQeJddFzIGM02oGV10Vaq8rVYJu3eAvf4Hdu/PcVUREREQKgJLrIqYgxmlt2zaUiVx4Idx0UxgTe9685MUsIiIiIkEyx7mW/VQQ47TWqAHjx0P37vD730OrVmESmn79oG9fOPLIgolVRERERPZSy3UJZgZXXBE6Qz76KNSuDTffDEcdBR07hmH8vs1xWh8RERERyS8l16VA3bpw5ZVhZsdly+Dee2HnTrjuujDj48knh9KT1avD2NoiIiIisn+UXBdTec3imJOGDeGPfww12V9+CbfeGlqvr7gCDjssdIRs3Bg6d4bevUMCPmoUPP88fPghfPONOkiKiIiI5EQ118VQ5iyO27aF95mzOEL+arWPPRZuvx1uuw1mzQpD961aFZLtVatgzhx46y3YujXrfkccAZdeCoMHw6GHFshHEhERESkRNENjMbQ/szjuL3fYvDkk26tWweLFYdr1Dz6AsmXhnHPg8svhzDPDexHZP5qhUUSk+Mjtmq2W62Jof2Zx3F9mYeSRGjWgadOwbMgQ+PprePJJePppeO01qF8ffvOb0KLdqFHBxyEiIiJSHKjmuhja31kcC9LRR8M998CKFfDSS9CyJdx1FzRpElqxn38e/vc/2LWr8GISERERSTW1XBdDI0ZkrbmGxGdxLGgVKoTJai68MJSqPPVUeGTWfleuHMbXbtkyjLXdqlV4Xbdu4ccqIiIikmxKrouhzMR1+PBQCtKwYUisD3TimQPVqBH8+c9hBJLZs8OskHPnhscbb8DYsXu3rVdvb5JdvjyUKxeeY19nPleuHIYMbNQoPA47TPXdIiIiUjQpuS6mCmIWx2QpWxbatQuPWN9/vzfhnjcvPBYvDmNuZ2SE59jXGRnxy0rKls2abDdsGJ7T0qBNm1AnLiIiIpIKSq5LuPT0otPCfcgh4XHqqYnvs3t3KH9ZsSKUnSxfHp4zX3/4YRjFJDMJb9gQevUKjxNPDK3fIiIiIoVFqUcJVlDjYadSmTJQrVoYqSRztJLsMjJg5Up47z2YOBH+/nd46CE46KAwVGCvXnD66VC1auHGLiIiIqWPxrkuwQpzPOyiZMsW+Pe/Q6L9xhvw449h5snTT4fTTgut3D/+uPexYcO+7489Fm65JSTmKjORwqBxrkVEig+Nc11KFeZ42EVJtWpwwQXhsXMnTJ0aEu2JE2HSpL3b1agBtWpB7drhccwx4blGDXjzzbB/mzZhFsvzzlOSLSIiInlTcl2CNWwYv+W6MMfDTrXy5aF79/B48MFQPlKlCtSsmXs99qhRYazuO+4IrdfHg8pRegAAG7RJREFUHx+S7HPPVZItIiIiOdMkMiXYiBEhkYyVqvGwiwIzOPxwqFMn746O5crBJZfAl1+GIQQ3boSePaF9+1BqklM11aZN8OmnYfbK3/8+dN7s0yeMkCJS1JnZmWb2lZktMrNhcdbXNLPXzWyOmc03s8Ex654ysx/M7IvCjVpEpGhJanKdwIW6p5nNNbPZZjbDzLrErKtlZi+Z2ZdmttDMTkhmrCVR//4wZkyosTYLz2PG7NuZMT091GeXKROe09NTEW3RVK4cDBoUkuynnoL160PrdceO8OKL8NxzcOONcPbZ4futWRN++Uu4/PKQYG/aBJMnh/KSiy8O08aLFEVmVhZ4FOgBNAP6mVmzbJtdBSxw99ZAN+B+M6sQrXsaOLNwohURKbqSVhYSc6E+DVgJTDezSe6+IGazd4FJ7u5m1gqYABwXrXsQeNvdL4ou3tnaYCUReY2HXRJGFCkM5cvD4MEwYAA8+2yY6r1377CuQoUwkkmXLtCixd5Ho0bhD5YffwxlJg8+CBMmhGT91ltLV3mOFAsdgEXuvhjAzMYDPYHYa7YD1c3MgGrAeiADwN2nmFnjwgxYRKQoSmbL9Z4LtbvvADIv1Hu4+xbfO1xJVcKFGzOrAZwE/CPaboe7b0hirKXW8OFZp1GH8H748NTEU9SVLw+XXgpffQXvvgsLF8LWrWFGyvR0+NOfQst2kyYhsYbQSXLkyDBhztVXh9buo4+Ga68NE+uIFBH1gRX/3969R1dVnvse/z5EINwkSlArSPCKmpAlMcLgiC2KpaJ1ez8kpacWWj3ivcfubVrpkF2PrbVa421QtdrtrmwdthYrVnErBznb7VEgQBKBKmyDFUEMFyHclMhz/pgzIQkryUyykrVW8vuMMcdaa655eTIz8uZZ73rm+zZ4vSFc19AjwGnARqASuMXdD7TlJGZ2bfhN5bLq6uqOxCsikpI6M7mO0lBjZpeZ2d+AvwIzwtUnANXA781shZn9zszijlKshrpjeuqIIh3Vp09wk+Spp0afqOboo6G0NCgN+d734NFH4YQT4Kc/DXq3RZIs3q26Te8u+BawEjgWOAN4JOwMiczdH3f3QncvHDp0aPsiFRFJYZ2ZXEdpqHH3ee5+KnApcFe4+jCgAJjj7mOA3cAhNdvh/mqoO6C50oR461WbnRgjRsATTwS93pdcAr/8ZVBCctZZwVjcRUUwc2bw7cF998GTT8K8efDmm1BeHsxWuWtX8zdVirTTBuC4Bq+HE/RQNzQd+LMH1gFVHCzlExEROncovigNdb2wXu9EM8sO993g7u+Gb/+JZpJr6Zi7725ccw3xRxRRbXbinXxyMNxfSQk8/HAwjfu2bcG13b49eF43rXs8vXsHJSdHHtn4MSsrSLy/+AL27Yv/+MUXwf6jRsHppx9cTjgBMjK67hpISlkKnGxmxwOfAEXAd5ps83dgEvAfZnY0MAr4sEujFBFJcZ2ZXLfaUJvZScB/hTc0FgB9gK3h64/NbJS7v0/QmK9GEq4uMb7jjqAUZMSIILFumjC3VJut5Lpj8vODnuym3IMe6m3bDs4eWfc83uOmTbBqVTDDZK9ewayUffse+piVFTzu3Rv0hj/zzMFz9u17aMI9ejSceKKS7u7O3WvN7EbgNSADeMrdV5nZdeH7vyX4dvFfzKyS4NvJ2919C4CZPUswgki2mW0A7nT3J5Pwo4iIJFWnTn9uZhcCpRxsqO9u2FCb2e3A94D9wF7gH939rXDfM4DfESTcHwLT3b3FylRNpdt5evWKX4ZgBgfadDuTpJqdO4MSldWrGy/r1x/cpn//4ENALBYMK3jGGUHSPSDunRDSHpr+XKTn2b9/Pxs2bGDfvn3JDkWakZmZyfDhw+ndu3ej9S212Z2aXHc1NdSdZ+TI+LM95uQ0TsLmzm29F1zSw+7dwfjeFRXBaCjl5cHjjh3B+2ZBaUssFvRwZ2cfnEq+6dKnT8vnEiXXIj1RVVUVgwYNYsiQIZim/0057s7WrVupqanh+OOPb/ReS222pj+XSKLUZqsuu3sZMADOPDNY6rgHH5zqEu3ycigrCybUaUn//kGSPXgwDBrU8pKZCTU1QXnL9u3BY7ylV6+gxGXw4OAx3jJwIOzfH5TA7NsX/3HvXjj22ODm0nPP1QcBEek6+/btY+TIkUqsU5SZMWTIENo6Gp2Sa4kkSm12W+qy1cOdnupm+szJgX/4h4Pr9+8/mAw3rAVvuGzbFpSg1NQEy6ZNB5/X1EBt7aHnaposn3zywYQaGifbH38MlZXB8x074pcxZWRAv35BAl/3mJkJr78Oc+YEx73oIrj0UpgyJUjORUQ6kxLr1Nae34+Sa4mstdkeo46ZrR7u7qd3bxg6NFjao250k5qaoEf58MODXuxe7Rws9MCBg0l73c2c/fo1Pyb53r3wxhvBkIcvvRSM4tK3L5x/Plx2WfBBQiN9iohIFEquJWFGjIhfl910zGyNPCJNmR3sRU6EXr2CXui6Hu7W9OsXzKx58cVBD/p//ie8+GKQbP/1r8Hxxo2DY44Jykb69Ak+UNQ9b7juqKPg+usT83OIiDSUyG99t27dyqRJkwD49NNPycjIoG6+kCVLltAnQo3c9OnTKSkpYdSoUc1u8+ijj5KVlcW0HvQPXjc0SsI07ZGGoNb28ccb//FHHXlEpSOSbO5BXfmLLwalIzU18OWXB5f9+xu/rq2FU06B999v+7l0Q6NIz7NmzRpOO+20SNtG/R/bHrNnz2bgwIH8+Mc/brTe3XF3erX3a8RuIt7vqaU2u2dfLUmoadOCP/KcnIO1ufH+6KPMClnXiHz0UZDg1JWOxJsVUjNHSmcxC4YdnD076M2uqAhGUPnwQ9iwATZvDurJd+8OEu2vvgrqvkVEEq2lb30Tad26deTl5XHddddRUFDApk2buPbaayksLCQ3N5ef//zn9dtOmDCBlStXUltbS1ZWFiUlJcRiMcaPH89nn30GwKxZsygtLa3fvqSkhLFjxzJq1CjefvttAHbv3s0VV1xBLBajuLiYwsJCVq5ceUhsd955J2eddVZ9fHUdxB988AHnnXcesViMgoIC1ofDmP3iF79g9OjRxGIx7kj0hWqBkmtJqGnTgqH5DhwIHuN9mr777uDTdkNNRx6J2oi0JQkX6Wy9emm0ERHpHFHva0qE1atX84Mf/IAVK1YwbNgw7rnnHpYtW0Z5eTmvv/46q1cfOq/fjh07+MY3vkF5eTnjx4/nqaeeintsd2fJkiX8+te/rk/UH374YY455hjKy8spKSlhxYoVcfe95ZZbWLp0KZWVlezYsYMFCxYAUFxczI9+9CPKy8t5++23Oeqoo5g/fz6vvvoqS5Ysoby8nNtuuy1BV6d1Sq6ly0Xp4Y7aiLQlCVfvtoiIpKso3/omyoknnshZZ51V//rZZ5+loKCAgoIC1qxZEze57tevH1OmTAHgzDPPrO89buryyy8/ZJu33nqLoqIiAGKxGLm5uXH3XbhwIWPHjiUWi7F48WJWrVrF9u3b2bJlCxdffDEQTPrSv39/3njjDWbMmEG/fv0AOPLII9t+IdpJNzRKUrQ28kjUmyOjJOEanURERNJdlPkmEmVAg+l3165dy4MPPsiSJUvIysriu9/9btwZJRveAJmRkUFt0/FVQ3379j1kmyj3/+3Zs4cbb7yR5cuXM2zYMGbNmlUfR7zh8tw9acMcqudaUlKU0hGI9km+LXVq6uEWEZFUFPW+pkTbuXMngwYN4vDDD2fTpk289tprCT/HhAkTeP755wGorKyM2zO+d+9eevXqRXZ2NjU1NbzwwgsAHHHEEWRnZzN//nwgmJhnz549TJ48mSeffJK9e/cCsG3btoTH3Rwl15KSojYiUZLwto6/rZsoRUQkFUW5rynRCgoKOP3008nLy+Oaa67h7LPPTvg5brrpJj755BPy8/O5//77ycvLY3CTsVSHDBnC1VdfTV5eHpdddhnjxo2rf2/u3Lncf//95OfnM2HCBKqrq/n2t7/NBRdcQGFhIWeccQYPPPBAwuNujobik7TX2pB9I0fGLzHJyQkap7ZuF3U4JA0lKG2hofhEep62DMXXndXW1lJbW0tmZiZr165l8uTJrF27lsOam/mri7V1KL7UiFqkA1qr345ap5aImyjr4lCdt4iISDS7du1i0qRJ1NbW4u489thjKZNYt4fKQqTbS+T42xAtCVedt4iISDRZWVmUlZVRXl5ORUUFkydPTnZIHaLkWnqERI2/DdGS8ETXeSsBFxERSQ9KrkVCibyJMmoveJQebt1oKSIikj6UXIs0EKWHO0oSHrUXPJElJuoFFxERST4l1yLt0FoSnsg670TOVqlecBERkc6l5FqkkySqzjsZN1qqF1xEpPubOHHiIZPClJaWcv3117e438CBAwHYuHEjV155ZbPHbm2ozdLSUvY0+Kd04YUX8vnnn0cJPaUpuRZJokSWmKgXXERE2qK4uJjnnnuu0brnnnuO4uLiSPsfe+yx/OlPf2r3+Zsm16+88gpZWVntPl6qUHItkmSJKjHpKb3gStRFpDu69VaYODGxy623tnzOK6+8kpdffpkvvvgCgPXr17Nx40YmTJhQP/Z0QUEBo0eP5i9/+csh+69fv568vDwgmJ68qKiI/Px8pk6dWj/tOMDMmTMpLCwkNzeXO++8E4CHHnqIjRs3cu6553LuuecCMHLkSLZs2QLAb37zG/Ly8sjLy6O0tLT+fKeddhrXXHMNubm5TJ48udF56syfP59x48YxZswYzj//fDZv3gwE42lPnz6d0aNHk5+fXz+F+oIFCygoKCAWizFp0qSWL1oEnZpcm9kFZva+ma0zs5I4719iZhVmttLMlpnZhCbvZ5jZCjN7uTPjFEl1XX2jZar2gqtcRUQkcYYMGcLYsWNZsGABEPRaT506FTMjMzOTefPmsXz5chYtWsRtt91GS7N6z5kzh/79+1NRUcEdd9xBWVlZ/Xt33303y5Yto6KigsWLF1NRUcHNN9/Msccey6JFi1i0aFGjY5WVlfH73/+ed999l3feeYcnnniCFStWALB27VpuuOEGVq1aRVZWVn2C3NCECRN45513WLFiBUVFRdx7770A3HXXXQwePJjKykoqKio477zzqK6u5pprruGFF16gvLycP/7xjx2+rrh7pyxABvBfwAlAH6AcOL3JNgM5OAV7PvC3Ju//L+DfgJejnPPMM890EWnZM8+45+S4mwWPzzwTf5v+/d2DFDZY+vdvvG1OTuP365acnMbHMou/nVnbjxVluyixt+VatGW7jgCWeSe1x1EX4ALgfWAdUBLn/cHA/LA9XwVMj7pvvEVttvR0q1evTnYI/oc//MGLiorc3T0Wi3lZWZm7u3/55Zd+ww03+OjRoz0Wi3lmZqZv2rTJ3d0HDBjg7u5VVVWem5vr7u6XXHKJL1y4sP64Y8aM8aVLl7q7+5w5c3zMmDE+evRoz87O9meffdbd3XNycry6urp+n7rXpaWl/rOf/ax+/axZs/zBBx/0qqoqP+mkk+rX33PPPX7XXXcd8jNVVFT4N7/5Tc/Ly/NTTjnFv/Wtb7m7e0FBgX/wwQeNtn3ppZf8O9/5TovXKN7vqaU2uzN7rscC69z9Q3f/EngOuKThBu6+KwwQYABQ/5HIzIYDFwG/68QYRXqcdO4FT1a5Sk/oLTezDOBRYApwOlBsZqc32ewGYLW7x4CJwP1m1ifivh2W7tdYJBVdeumlLFy4kOXLl7N3714KCgoAmDt3LtXV1ZSVlbFy5UqOPvpo9u3b1+KxzOyQdVVVVdx3330sXLiQiooKLrroolaPczA1PFTfvn3rn2dkZFBbW3vINjfddBM33ngjlZWVPPbYY/Xnc/dDYoy3rqM6M7keBnzc4PWGcF0jZnaZmf0N+Cswo8FbpcA/AQdaOomZXRuWlCyrrq7ueNQiAqRmLXhXl6tE3a4tN3emsFY7RAg6QAZZ8J9oILANqI24b4d0k2ssknIGDhzIxIkTmTFjRqMbGXfs2MFRRx1F7969WbRoER999FGLx/n617/O3PAP8r333qOiogKAnTt3MmDAAAYPHszmzZt59dVX6/cZNGgQNTU1cY/14osvsmfPHnbv3s28efM455xzIv9MO3bsYNiwIOV8+umn69dPnjyZRx55pP719u3bGT9+PIsXL6aqqgqAbdu2RT5PczozuY73MeCQjyLuPs/dTwUuBe4CMLNvA5+5e1nT7ePs/7i7F7p74dChQzsas4i0QVf3gnf1TZtRt4uaqKe4KB0ijwCnARuBSuAWdz8QcV+g/R0i3eQai6Sk4uJiysvLKSoqql83bdo0li1bRmFhIXPnzuXUU09t8RgzZ85k165d5Ofnc++99zJ27FgAYrEYY8aMITc3lxkzZnD22WfX73PttdcyZcqU+hsa6xQUFPD973+fsWPHMm7cOH74wx8yZsyYyD/P7NmzueqqqzjnnHPIzs6uXz9r1iy2b99OXl4esViMRYsWMXToUB5//HEuv/xyYrEYU6dOjXyeZjVXL9LRBRgPvNbg9U+An7SyTxWQDfySoHFeD3wK7AGeae2cqt8TSV+Jqn+OWnOdyDrvKHXlrSHJNdfAVcDvGrz+H8DDTba5EniAoPPkpLDNPjzKvvGWtrTZibjGIqkmFWqupXWpVHO9FDjZzI43sz5AEfBSww3M7KTw60XMrIDgxset7v4Tdx/u7iPD/f6Pu3+3E2MVkSSL0gseZbtElqtE3S5qb3mK2wAc1+D1cIIe6oamA38O/7esI0iuT424b4d0k2ssIj1ApyXX7l4L3Ai8BqwBnnf3VWZ2nZldF252BfCema0kuBlmavhpQESk3RJVrhJ1u6iJeoprtUME+DswCcDMjgZGAR9G3LdDusk1FpEe4LDOPLi7vwK80mTdbxs8/xXwq1aO8SbwZieEJyI93LRpzfeQt2W7uvfuuCOoxR4xIkj6ohw7Vbh7rZnVdYhkAE/VdYiE7/+W4L6YfzGzSoLSkNvdfQtAvH0TGV93uMYi8XgnjFYhidOePl/rTh3FhYWF3to89iIiqcjMyty9MNlxdCW12dLTVVVVMWjQIIYMGaIEOwW5O1u3bqWmpobjjz++0Xsttdmd2nMtIiIiIvENHz6cDRs2oKGEU1dmZibDhw9v0z5KrkVERESSoHfv3of0iEr668zRQkREREREehQl1yIiIiIiCaLkWkREREQkQbrVaCFmVg181GBVNrAlSeEkQjrHn86xg+JPpnSOHdoff467D010MKlMbXbKSef40zl2UPzJlPA2u1sl102Z2bJ0HtoqneNP59hB8SdTOscO6R9/MqX7tVP8yZPOsYPiT6bOiF1lISIiIiIiCaLkWkREREQkQbp7cv14sgPooHSOP51jB8WfTOkcO6R//MmU7tdO8SdPOscOij+ZEh57t665FhERERHpSt2951pEREREpMsouRYRERERSZBumVyb2QVm9r6ZrTOzkmTH01Zmtt7MKs1spZktS3Y8rTGzp8zsMzN7r8G6I83sdTNbGz4ekcwYW9JM/LPN7JPwd7DSzC5MZozNMbPjzGyRma0xs1Vmdku4Pi2ufwvxp/z1N7NMM1tiZuVh7P8crk+La59q0rndVpvdtdRmJ086t9nQde12t6u5NrMM4APgm8AGYClQ7O6rkxpYG5jZeqDQ3dNiQHYz+zqwC/hXd88L190LbHP3e8J/lEe4++3JjLM5zcQ/G9jl7vclM7bWmNnXgK+5+3IzGwSUAZcC3ycNrn8L8f93Uvz6m5kBA9x9l5n1Bt4CbgEuJw2ufSpJ93ZbbXbXUpudPOncZkPXtdvdsed6LLDO3T909y+B54BLkhxTt+bu/xfY1mT1JcDT4fOnCf74UlIz8acFd9/k7svD5zXAGmAYaXL9W4g/5XlgV/iyd7g4aXLtU4za7S6kNjt51GYnV1e1290xuR4GfNzg9QbS6BcfcuDfzazMzK5NdjDtdLS7b4LgjxE4KsnxtMeNZlYRfgWZkl/RNWRmI4ExwLuk4fVvEj+kwfU3swwzWwl8Brzu7ml57VNAurfbarNTQ8q3GQ2pzU6Ormi3u2NybXHWpVvty9nuXgBMAW4IvwKTrjUHOBE4A9gE3J/ccFpmZgOBF4Bb3X1nsuNpqzjxp8X1d/ev3P0MYDgw1szykh1Tmkr3dlttdvKlRZtRR2128nRFu90dk+sNwHENXg8HNiYplnZx943h42fAPIKvTNPN5rA2q65G67Mkx9Mm7r45/AM8ADxBCv8OwrqxF4C57v7ncHXaXP948afT9Qdw98+BN4ELSKNrn0LSut1Wm5186dRmqM1ODZ3ZbnfH5HopcLKZHW9mfYAi4KUkxxSZmQ0IbxLAzAYAk4H3Wt4rJb0EXB0+vxr4SxJjabO6P7LQZaTo7yC8OeNJYI27/6bBW2lx/ZuLPx2uv5kNNbOs8Hk/4Hzgb6TJtU8xadtuq81ODenQZoDa7GTrqna7240WAhAOAVMKZABPufvdSQ4pMjM7gaDnA+Aw4N9SPX4zexaYCGQDm4E7gReB54ERwN+Bq9w9JW9AaSb+iQRfbzmwHvifdfVYqcTMJgD/AVQCB8LVPyWogUv5699C/MWk+PU3s3yCG18yCDoqnnf3n5vZENLg2qeadG231WZ3PbXZyZPObTZ0XbvdLZNrEREREZFk6I5lISIiIiIiSaHkWkREREQkQZRci4iIiIgkiJJrEREREZEEUXItIiIiIpIgSq6lRzCzr8xsZYOlJIHHHmlmKTmmp4hIOlKbLenssGQHINJF9obTnYqISOpTmy1pSz3X0qOZ2Xoz+5WZLQmXk8L1OWa20MwqwscR4fqjzWyemZWHy38LD5VhZk+Y2Soz+/dw5ifM7GYzWx0e57kk/ZgiIt2C2mxJB0qupafo1+QrxqkN3tvp7mOBRwhmiCN8/q/ung/MBR4K1z8ELHb3GFAArArXnww86u65wOfAFeH6EmBMeJzrOuuHExHpZtRmS9rSDI3SI5jZLncfGGf9euA8d//QzHoDn7r7EDPbAnzN3feH6ze5e7aZVQPD3f2LBscYCbzu7ieHr28Herv7/zazBcAugqmFX3T3XZ38o4qIpD212ZLO1HMtAt7M8+a2ieeLBs+/4uD9DBcBjwJnAmVmpvscREQ6Rm22pDQl1yIwtcHj/wufvw0Uhc+nAW+FzxcCMwHMLMPMDm/uoGbWCzjO3RcB/wRkAYf0xIiISJuozZaUpk9k0lP0M7OVDV4vcPe6oZ36mtm7BB82i8N1NwNPmdk/AtXA9HD9LcDjZvYDgt6OmcCmZs6ZATxjZoMBAx5w988T9hOJiHRfarMlbanmWnq0sH6v0N23JDsWERFpmdpsSQcqCxERERERSRD1XIuIiIiIJIh6rkVEREREEkTJtYiIiIhIgii5FhERERFJECXXIiIiIiIJouRaRERERCRB/j8ZYu8JK/F2dAAAAABJRU5ErkJggg==\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"show_report(X_test, y_test, y_pred)\nshow_matrix(y_test, y_pred)","execution_count":83,"outputs":[{"output_type":"stream","text":"              precision    recall  f1-score   support\n\n           0       0.87      0.97      0.92      1583\n           1       0.78      0.47      0.58       417\n\n    accuracy                           0.86      2000\n   macro avg       0.83      0.72      0.75      2000\nweighted avg       0.85      0.86      0.85      2000\n\n","name":"stdout"},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{},"cell_type":"markdown","source":"**深层神经网络**"},{"metadata":{"trusted":true},"cell_type":"code","source":"ann = Sequential() # 创建一个序贯ANN模型\nann.add(Dense(units=12, input_dim=12, activation = 'relu')) # 添加输入层\nann.add(Dense(units=24, activation = 'relu')) # 添加隐层\nann.add(Dense(units=48, activation = 'relu')) # 添加隐层\nann.add(Dense(units=96, activation = 'relu')) # 添加隐层\nann.add(Dense(units=192, activation = 'relu')) # 添加隐层\nann.add(Dense(units=1, activation = 'sigmoid')) # 添加输出层\n# 编译神经网络，指定优化器，损失函数，以及评估指标\nann.compile(optimizer = 'RMSprop', # 优化器\n            loss = 'binary_crossentropy', # 损失函数\n            metrics = ['acc']) # 评估指标\nhistory = ann.fit(X_train, y_train, # 指定训练集\n                  epochs=30,        # 指定训练的轮次\n                  batch_size=64,    # 指定数据批量\n                  validation_data=(X_test, y_test)) # 指定验证集\ny_pred = ann.predict(X_test,batch_size=10) # 预测测试集的标签\ny_pred = np.round(y_pred) # 将分类概率值转换成0/1整数值","execution_count":85,"outputs":[{"output_type":"stream","text":"Train on 8000 samples, validate on 2000 samples\nEpoch 1/30\n8000/8000 [==============================] - 0s 50us/step - loss: 0.4758 - acc: 0.7956 - val_loss: 0.4554 - val_acc: 0.8040\nEpoch 2/30\n8000/8000 [==============================] - 0s 28us/step - loss: 0.4346 - acc: 0.8101 - val_loss: 0.4333 - val_acc: 0.8150\nEpoch 3/30\n8000/8000 [==============================] - 0s 27us/step - loss: 0.4149 - acc: 0.8191 - val_loss: 0.4188 - val_acc: 0.8235\nEpoch 4/30\n8000/8000 [==============================] - 0s 27us/step - loss: 0.4004 - acc: 0.8271 - val_loss: 0.4098 - val_acc: 0.8315\nEpoch 5/30\n8000/8000 [==============================] - 0s 28us/step - loss: 0.3830 - acc: 0.8388 - val_loss: 0.3891 - val_acc: 0.8415\nEpoch 6/30\n8000/8000 [==============================] - 0s 28us/step - loss: 0.3675 - acc: 0.8469 - val_loss: 0.3828 - val_acc: 0.8475\nEpoch 7/30\n8000/8000 [==============================] - 0s 27us/step - loss: 0.3542 - acc: 0.8539 - val_loss: 0.3718 - val_acc: 0.8485\nEpoch 8/30\n8000/8000 [==============================] - 0s 27us/step - loss: 0.3470 - acc: 0.8565 - val_loss: 0.3639 - val_acc: 0.8530\nEpoch 9/30\n8000/8000 [==============================] - 0s 27us/step - loss: 0.3405 - acc: 0.8584 - val_loss: 0.3809 - val_acc: 0.8525\nEpoch 10/30\n8000/8000 [==============================] - 0s 27us/step - loss: 0.3385 - acc: 0.8619 - val_loss: 0.3594 - val_acc: 0.8555\nEpoch 11/30\n8000/8000 [==============================] - 0s 28us/step - loss: 0.3338 - acc: 0.8648 - val_loss: 0.3621 - val_acc: 0.8520\nEpoch 12/30\n8000/8000 [==============================] - 0s 28us/step - loss: 0.3303 - acc: 0.8641 - val_loss: 0.3594 - val_acc: 0.8540\nEpoch 13/30\n8000/8000 [==============================] - 0s 28us/step - loss: 0.3289 - acc: 0.8631 - val_loss: 0.3584 - val_acc: 0.8545\nEpoch 14/30\n8000/8000 [==============================] - 0s 27us/step - loss: 0.3279 - acc: 0.8669 - val_loss: 0.3608 - val_acc: 0.8535\nEpoch 15/30\n8000/8000 [==============================] - 0s 27us/step - loss: 0.3239 - acc: 0.8680 - val_loss: 0.3600 - val_acc: 0.8565\nEpoch 16/30\n8000/8000 [==============================] - 0s 27us/step - loss: 0.3220 - acc: 0.8687 - val_loss: 0.3598 - val_acc: 0.8540\nEpoch 17/30\n8000/8000 [==============================] - 0s 27us/step - loss: 0.3205 - acc: 0.8696 - val_loss: 0.3596 - val_acc: 0.8585\nEpoch 18/30\n8000/8000 [==============================] - 0s 27us/step - loss: 0.3197 - acc: 0.8679 - val_loss: 0.3739 - val_acc: 0.8565\nEpoch 19/30\n8000/8000 [==============================] - 0s 29us/step - loss: 0.3168 - acc: 0.8690 - val_loss: 0.3664 - val_acc: 0.8590\nEpoch 20/30\n8000/8000 [==============================] - 0s 28us/step - loss: 0.3161 - acc: 0.8712 - val_loss: 0.3663 - val_acc: 0.8590\nEpoch 21/30\n8000/8000 [==============================] - 0s 27us/step - loss: 0.3144 - acc: 0.8736 - val_loss: 0.3719 - val_acc: 0.8620\nEpoch 22/30\n8000/8000 [==============================] - 0s 28us/step - loss: 0.3129 - acc: 0.8715 - val_loss: 0.3636 - val_acc: 0.8570\nEpoch 23/30\n8000/8000 [==============================] - 0s 28us/step - loss: 0.3109 - acc: 0.8763 - val_loss: 0.3672 - val_acc: 0.8595\nEpoch 24/30\n8000/8000 [==============================] - 0s 28us/step - loss: 0.3108 - acc: 0.8771 - val_loss: 0.3659 - val_acc: 0.8575\nEpoch 25/30\n8000/8000 [==============================] - 0s 28us/step - loss: 0.3098 - acc: 0.8763 - val_loss: 0.3652 - val_acc: 0.8590\nEpoch 26/30\n8000/8000 [==============================] - 0s 27us/step - loss: 0.3066 - acc: 0.8774 - val_loss: 0.3802 - val_acc: 0.8585\nEpoch 27/30\n8000/8000 [==============================] - 0s 27us/step - loss: 0.3069 - acc: 0.8752 - val_loss: 0.3703 - val_acc: 0.8595\nEpoch 28/30\n8000/8000 [==============================] - 0s 28us/step - loss: 0.3058 - acc: 0.8769 - val_loss: 0.3707 - val_acc: 0.8520\nEpoch 29/30\n8000/8000 [==============================] - 0s 27us/step - loss: 0.3035 - acc: 0.8770 - val_loss: 0.3691 - val_acc: 0.8605\nEpoch 30/30\n8000/8000 [==============================] - 0s 27us/step - loss: 0.3022 - acc: 0.8777 - val_loss: 0.3823 - val_acc: 0.8485\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"show_history(history)","execution_count":86,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 864x288 with 2 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\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"show_report(X_test, y_test, y_pred)\nshow_matrix(y_test, y_pred)","execution_count":87,"outputs":[{"output_type":"stream","text":"              precision    recall  f1-score   support\n\n           0       0.88      0.94      0.91      1583\n           1       0.69      0.49      0.58       417\n\n    accuracy                           0.85      2000\n   macro avg       0.78      0.72      0.74      2000\nweighted avg       0.84      0.85      0.84      2000\n\n","name":"stdout"},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{},"cell_type":"markdown","source":"**尝试不同的优化器**"},{"metadata":{"trusted":true},"cell_type":"code","source":"ann = Sequential() # 创建一个序贯ANN模型\nann.add(Dense(units=12, input_dim=12, activation = 'relu')) # 添加输入层\nann.add(Dense(units=24, activation = 'relu')) # 添加隐层\nann.add(Dense(units=48, activation = 'relu')) # 添加隐层\nann.add(Dense(units=96, activation = 'relu')) # 添加隐层\nann.add(Dense(units=192, activation = 'relu')) # 添加隐层\nann.add(Dense(units=1, activation = 'sigmoid')) # 添加输出层\n# 编译神经网络，指定优化器，损失函数，以及评估标准\nann.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['acc'])\nhistory = ann.fit(X_train, y_train, epochs=30, batch_size=64, validation_data=(X_test, y_test))\ny_pred = ann.predict(X_test,batch_size=10) # 预测测试集的标签\ny_pred = np.round(y_pred) # 将分类概率值转换成0/1整数值","execution_count":88,"outputs":[{"output_type":"stream","text":"Train on 8000 samples, validate on 2000 samples\nEpoch 1/30\n8000/8000 [==============================] - 0s 58us/step - loss: 0.4993 - acc: 0.7850 - val_loss: 0.4682 - val_acc: 0.7915\nEpoch 2/30\n8000/8000 [==============================] - 0s 29us/step - loss: 0.4474 - acc: 0.7977 - val_loss: 0.4468 - val_acc: 0.7945\nEpoch 3/30\n8000/8000 [==============================] - 0s 30us/step - loss: 0.4253 - acc: 0.8164 - val_loss: 0.4285 - val_acc: 0.8275\nEpoch 4/30\n8000/8000 [==============================] - 0s 30us/step - loss: 0.4116 - acc: 0.8274 - val_loss: 0.4272 - val_acc: 0.8255\nEpoch 5/30\n8000/8000 [==============================] - 0s 29us/step - loss: 0.4046 - acc: 0.8310 - val_loss: 0.4138 - val_acc: 0.8315\nEpoch 6/30\n8000/8000 [==============================] - 0s 30us/step - loss: 0.3957 - acc: 0.8344 - val_loss: 0.4123 - val_acc: 0.8315\nEpoch 7/30\n8000/8000 [==============================] - 0s 30us/step - loss: 0.3910 - acc: 0.8370 - val_loss: 0.4103 - val_acc: 0.8345\nEpoch 8/30\n8000/8000 [==============================] - 0s 30us/step - loss: 0.3877 - acc: 0.8378 - val_loss: 0.4028 - val_acc: 0.8405\nEpoch 9/30\n8000/8000 [==============================] - 0s 30us/step - loss: 0.3818 - acc: 0.8405 - val_loss: 0.3992 - val_acc: 0.8385\nEpoch 10/30\n8000/8000 [==============================] - 0s 29us/step - loss: 0.3764 - acc: 0.8440 - val_loss: 0.3980 - val_acc: 0.8465\nEpoch 11/30\n8000/8000 [==============================] - 0s 29us/step - loss: 0.3720 - acc: 0.8440 - val_loss: 0.3942 - val_acc: 0.8435\nEpoch 12/30\n8000/8000 [==============================] - 0s 30us/step - loss: 0.3674 - acc: 0.8474 - val_loss: 0.3897 - val_acc: 0.8385\nEpoch 13/30\n8000/8000 [==============================] - 0s 30us/step - loss: 0.3614 - acc: 0.8501 - val_loss: 0.3813 - val_acc: 0.8480\nEpoch 14/30\n8000/8000 [==============================] - 0s 30us/step - loss: 0.3512 - acc: 0.8536 - val_loss: 0.3819 - val_acc: 0.8405\nEpoch 15/30\n8000/8000 [==============================] - 0s 30us/step - loss: 0.3467 - acc: 0.8579 - val_loss: 0.3754 - val_acc: 0.8495\nEpoch 16/30\n8000/8000 [==============================] - 0s 29us/step - loss: 0.3362 - acc: 0.8625 - val_loss: 0.3651 - val_acc: 0.8545\nEpoch 17/30\n8000/8000 [==============================] - 0s 29us/step - loss: 0.3309 - acc: 0.8621 - val_loss: 0.3609 - val_acc: 0.8580\nEpoch 18/30\n8000/8000 [==============================] - 0s 29us/step - loss: 0.3253 - acc: 0.8645 - val_loss: 0.3678 - val_acc: 0.8520\nEpoch 19/30\n8000/8000 [==============================] - 0s 29us/step - loss: 0.3235 - acc: 0.8627 - val_loss: 0.3651 - val_acc: 0.8550\nEpoch 20/30\n8000/8000 [==============================] - 0s 29us/step - loss: 0.3213 - acc: 0.8687 - val_loss: 0.3555 - val_acc: 0.8545\nEpoch 21/30\n8000/8000 [==============================] - 0s 29us/step - loss: 0.3195 - acc: 0.8687 - val_loss: 0.3592 - val_acc: 0.8550\nEpoch 22/30\n8000/8000 [==============================] - 0s 29us/step - loss: 0.3153 - acc: 0.8706 - val_loss: 0.3658 - val_acc: 0.8540\nEpoch 23/30\n8000/8000 [==============================] - 0s 29us/step - loss: 0.3135 - acc: 0.8721 - val_loss: 0.3575 - val_acc: 0.8545\nEpoch 24/30\n8000/8000 [==============================] - 0s 30us/step - loss: 0.3105 - acc: 0.8715 - val_loss: 0.3621 - val_acc: 0.8540\nEpoch 25/30\n8000/8000 [==============================] - 0s 29us/step - loss: 0.3087 - acc: 0.8717 - val_loss: 0.3653 - val_acc: 0.8530\nEpoch 26/30\n8000/8000 [==============================] - 0s 29us/step - loss: 0.3058 - acc: 0.8726 - val_loss: 0.3656 - val_acc: 0.8540\nEpoch 27/30\n8000/8000 [==============================] - 0s 29us/step - loss: 0.3050 - acc: 0.8735 - val_loss: 0.3725 - val_acc: 0.8520\nEpoch 28/30\n8000/8000 [==============================] - 0s 30us/step - loss: 0.3039 - acc: 0.8719 - val_loss: 0.3631 - val_acc: 0.8485\nEpoch 29/30\n8000/8000 [==============================] - 0s 30us/step - loss: 0.3009 - acc: 0.8735 - val_loss: 0.3709 - val_acc: 0.8495\nEpoch 30/30\n8000/8000 [==============================] - 0s 31us/step - loss: 0.2987 - acc: 0.8754 - val_loss: 0.3645 - val_acc: 0.8495\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"show_history(history)","execution_count":89,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 864x288 with 2 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\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"show_report(X_test, y_test, y_pred)\nshow_matrix(y_test, y_pred)","execution_count":90,"outputs":[{"output_type":"stream","text":"              precision    recall  f1-score   support\n\n           0       0.87      0.96      0.91      1583\n           1       0.73      0.44      0.55       417\n\n    accuracy                           0.85      2000\n   macro avg       0.80      0.70      0.73      2000\nweighted avg       0.84      0.85      0.83      2000\n\n","name":"stdout"},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{},"cell_type":"markdown","source":"**添加Dropout层**"},{"metadata":{"trusted":true},"cell_type":"code","source":"# from keras.layers import Dropout # 导入Dropout\n# ann = Sequential() # 创建一个序贯ANN模型\n# ann.add(Dense(units=12, input_dim=12, activation = 'relu')) # 添加输入层\n# ann.add(Dense(units=24, activation = 'relu')) # 添加隐层\n# ann.add(Dropout(0.5)) # 添加Dropout\n# ann.add(Dense(units=48, activation = 'relu')) # 添加隐层\n# ann.add(Dropout(0.5)) # 添加Dropout\n# ann.add(Dense(units=96, activation = 'relu')) # 添加隐层\n# ann.add(Dropout(0.5)) # 添加Dropout\n# ann.add(Dense(units=192, activation = 'relu')) # 添加隐层\n# ann.add(Dropout(0.5)) # 添加Dropout\n# ann.add(Dense(units=1, activation = 'sigmoid')) # 添加输出层\n# # 编译神经网络，指定优化器，损失函数，以及评估标准\n# ann.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['acc']) \n# history = ann.fit(X_train, y_train, epochs=30, batch_size=64, validation_data=(X_test, y_test))\n# y_pred = ann.predict(X_test,batch_size=10) # 预测测试集的标签\n# y_pred = np.round(y_pred) # 将分类概率值转换成0/1整数值","execution_count":91,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"from keras.layers import Dropout # 导入Dropout\nann = Sequential() # 创建一个序贯ANN模型\nann.add(Dense(units=12, input_dim=12, activation = 'relu')) # 添加输入层\nann.add(Dense(units=24, activation = 'relu')) # 添加隐层\nann.add(Dropout(0.5)) # 添加Dropout\nann.add(Dense(units=48, activation = 'relu')) # 添加隐层\nann.add(Dropout(0.5)) # 添加Dropout\nann.add(Dense(units=96, activation = 'relu')) # 添加隐层\nann.add(Dropout(0.5)) # 添加Dropout\nann.add(Dense(units=192, activation = 'relu')) # 添加隐层\nann.add(Dropout(0.5)) # 添加Dropout\nann.add(Dense(units=1, activation = 'sigmoid')) # 添加输出层\nann.compile(optimizer = 'adam', # 优化器\n              loss = 'binary_crossentropy', #损失函数 \n              metrics = ['acc']) # 评估指标\nhistory = ann.fit(X_train, y_train, epochs=30, batch_size=64, validation_data=(X_test, y_test))\ny_pred = ann.predict(X_test,batch_size=10) # 预测测试集的标签\ny_pred = np.round(y_pred) # 将分类概率值转换成0/1整数值","execution_count":92,"outputs":[{"output_type":"stream","text":"Train on 8000 samples, validate on 2000 samples\nEpoch 1/30\n8000/8000 [==============================] - 1s 87us/step - loss: 0.5409 - acc: 0.7914 - val_loss: 0.5242 - val_acc: 0.7915\nEpoch 2/30\n8000/8000 [==============================] - 0s 39us/step - loss: 0.5004 - acc: 0.7975 - val_loss: 0.5043 - val_acc: 0.7915\nEpoch 3/30\n8000/8000 [==============================] - 0s 39us/step - loss: 0.4873 - acc: 0.7975 - val_loss: 0.4964 - val_acc: 0.7915\nEpoch 4/30\n8000/8000 [==============================] - 0s 38us/step - loss: 0.4774 - acc: 0.7975 - val_loss: 0.5016 - val_acc: 0.7915\nEpoch 5/30\n8000/8000 [==============================] - 0s 39us/step - loss: 0.4655 - acc: 0.7975 - val_loss: 0.4887 - val_acc: 0.7915\nEpoch 6/30\n8000/8000 [==============================] - 0s 38us/step - loss: 0.4585 - acc: 0.7975 - val_loss: 0.4998 - val_acc: 0.7915\nEpoch 7/30\n8000/8000 [==============================] - 0s 39us/step - loss: 0.4524 - acc: 0.7975 - val_loss: 0.4847 - val_acc: 0.7915\nEpoch 8/30\n8000/8000 [==============================] - 0s 38us/step - loss: 0.4470 - acc: 0.7975 - val_loss: 0.5006 - val_acc: 0.7915\nEpoch 9/30\n8000/8000 [==============================] - 0s 38us/step - loss: 0.4420 - acc: 0.7975 - val_loss: 0.5088 - val_acc: 0.7915\nEpoch 10/30\n8000/8000 [==============================] - 0s 38us/step - loss: 0.4424 - acc: 0.7975 - val_loss: 0.4871 - val_acc: 0.7915\nEpoch 11/30\n8000/8000 [==============================] - 0s 38us/step - loss: 0.4348 - acc: 0.7980 - val_loss: 0.5012 - val_acc: 0.7915\nEpoch 12/30\n8000/8000 [==============================] - 0s 39us/step - loss: 0.4321 - acc: 0.8055 - val_loss: 0.4817 - val_acc: 0.8115\nEpoch 13/30\n8000/8000 [==============================] - 0s 39us/step - loss: 0.4310 - acc: 0.8061 - val_loss: 0.4954 - val_acc: 0.8275\nEpoch 14/30\n8000/8000 [==============================] - 0s 39us/step - loss: 0.4231 - acc: 0.8109 - val_loss: 0.4799 - val_acc: 0.8355\nEpoch 15/30\n8000/8000 [==============================] - 0s 39us/step - loss: 0.4220 - acc: 0.8120 - val_loss: 0.4752 - val_acc: 0.8350\nEpoch 16/30\n8000/8000 [==============================] - 0s 38us/step - loss: 0.4114 - acc: 0.8170 - val_loss: 0.4618 - val_acc: 0.8370\nEpoch 17/30\n8000/8000 [==============================] - 0s 39us/step - loss: 0.4083 - acc: 0.8167 - val_loss: 0.4820 - val_acc: 0.8390\nEpoch 18/30\n8000/8000 [==============================] - 0s 38us/step - loss: 0.4048 - acc: 0.8206 - val_loss: 0.4557 - val_acc: 0.8410\nEpoch 19/30\n8000/8000 [==============================] - 0s 38us/step - loss: 0.4004 - acc: 0.8191 - val_loss: 0.4483 - val_acc: 0.8465\nEpoch 20/30\n8000/8000 [==============================] - 0s 39us/step - loss: 0.3992 - acc: 0.8184 - val_loss: 0.4263 - val_acc: 0.8480\nEpoch 21/30\n8000/8000 [==============================] - 0s 38us/step - loss: 0.3932 - acc: 0.8219 - val_loss: 0.4261 - val_acc: 0.8465\nEpoch 22/30\n8000/8000 [==============================] - 0s 38us/step - loss: 0.3911 - acc: 0.8241 - val_loss: 0.4401 - val_acc: 0.8510\nEpoch 23/30\n8000/8000 [==============================] - 0s 38us/step - loss: 0.3833 - acc: 0.8240 - val_loss: 0.4155 - val_acc: 0.8575\nEpoch 24/30\n8000/8000 [==============================] - 0s 38us/step - loss: 0.3839 - acc: 0.8255 - val_loss: 0.4067 - val_acc: 0.8580\nEpoch 25/30\n8000/8000 [==============================] - 0s 38us/step - loss: 0.3797 - acc: 0.8372 - val_loss: 0.4069 - val_acc: 0.8565\nEpoch 26/30\n8000/8000 [==============================] - 0s 38us/step - loss: 0.3810 - acc: 0.8485 - val_loss: 0.4093 - val_acc: 0.8545\nEpoch 27/30\n8000/8000 [==============================] - 0s 38us/step - loss: 0.3760 - acc: 0.8475 - val_loss: 0.4468 - val_acc: 0.8530\nEpoch 28/30\n8000/8000 [==============================] - 0s 38us/step - loss: 0.3760 - acc: 0.8454 - val_loss: 0.4057 - val_acc: 0.8545\nEpoch 29/30\n8000/8000 [==============================] - 0s 40us/step - loss: 0.3728 - acc: 0.8495 - val_loss: 0.4126 - val_acc: 0.8555\nEpoch 30/30\n8000/8000 [==============================] - 0s 39us/step - loss: 0.3691 - acc: 0.8479 - val_loss: 0.4344 - val_acc: 0.8535\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"show_history(history)","execution_count":93,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 864x288 with 2 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GfTuDePHQ+3a9m2tdm277/ncrijLbixDNMyfP5/LL7+cn376iZo1a/LAAw+QnJzM7Nmz+eSTT5g/f/8CF5s2baJ9+/bMnj2bNm3a8Pzzz2e6b1Vl5syZPPTQQ3saPE888QSHHnoos2fPZujQofz000/ZxrdixQruvPNOpk2bxk8//cQ333zDe++9R0pKCuvWrWPu3Ln8/PPPXHLJJQA8+OCDzJo1i9mzZzNmzJh8vjouv/z9bK677jp++OEH5s6dy6ZNm/joo48A6NWrFzfccAOzZ8/m22+/5eCDD+bdd9/lww8/ZObMmcyePZubbropQq+OKwxUYfhwqFMHHnjAGtAHH5xzr3XVqnDnnfbFdswYWLXKGuaNG8NLL0GYmVfFhje6M9G7t+U0pec2eYPbFXUFPZahbt26HH/88Xvuv/rqq7Ro0YIWLVqwYMGCTBspZcuWpUuXLgAcd9xxe3rnMurRo8d+63z99df07NkTgKZNm9KwYcNs45sxYwYdO3akevXqlC5dmosuuojp06dz9NFHs3DhQq677jqmTp1KpUqVAGjYsCF9+vQhKSnJJwWJA/5+Np999hktW7akadOmfPnll8ybN48NGzawbt06zjrrLMAmsylXrhyffvopl112GWXLlgWgatWquX8hXKH10Ufwww9w++2Ql1NYuXIwaBAsWmRXlEqVgr59oWHDfdNVijtvdDvnshyzEK2xDAcdtHcm8EWLFvH444/z+eefM2fOHDp37pxpnd8DDjhgz+8lS5YkNTU1030feOCB+62Tflk9XFmtX61aNebMmUO7du0YPXo0V111FQBTp05lwIABzJw5k8TERHbv3p2r47nI8vczbN++ncGDBzNlyhTmzJnDZZddtieOzMr6qaqX+yumVOGee+zKfnDxLs9KlYKLLoJZs+Ddd23ZaafZgMvVq/Mfa2HnjW7nXEzHMmzevJkKFSpQsWJFVq1axdSpUyN+jHbt2jF58mQA5s6dm2nPY6jWrVszbdo01q9fT2pqKpMmTaJ9+/asXbsWVeX888/nnnvu4ccff2T37t2sWLGCjh078tBDD7F27Vq2Z8xtcAXK38/wzz//UKJECapXr86WLVt44403AKhSpQrVq1fn3aBFtGPHDrZv306nTp147rnn+OeffwD4+++/Ix63i0+ffAIzZsBtt0HId8F8EYEzz7TKJnfdBZMn2wDM8eNjn++dlgax6hfxRrdzLqZjGVq0aEGDBg1o1KgR/fv3p23bthE/xjXXXMOff/5JkyZNeOSRR2jUqNGe1JDM1KpVi+HDh9OhQweaNWtG69at6dq1K3/88QcnnXQSzZo1o3///tx3332kpqZy0UUX0aRJE1q0aMGQIUOoUKFCxJ+DC5+/n+2qzKWXXkqjRo3o3r07rVq12vNYUlISjzzyCE2aNKFdu3asXbuWM888k86dO5OYmEizZs147LHHIh63iz/pvdxHHGHpIJFWpoztf84caNoUrroKTjyx4MsM/vMPvPce9O9v5Q+rV4/NFwDJ7WXXwigxMVGTk5NjHYZzBWrBggXUr18/1mHEhdTUVFJTUylTpgyLFi2iU6dOLFq0iFKl4qtqamZ/MxFJUdViNblMZudsfz/vFe/vZ/9bFR6ffmrpH2PHwtVXR/dYqjBhAtx0k020c9NN1gue8apUpPz9t1VkeestmDoVtm2DChWsvOFff1mFlhNOgKeftoGfkZTVeTs+/kOdcy6Ktm7dyimnnEJqaiqqyrhx4+KmgeJcbvn72UVCei93zZpw+eXRP56ITaxz5plWfvC//4X//c8a/GecEZljLF9ujey33oLp0y2N5PDDLVf9nHOgQwerMR76BaBFCyuTeNddEDI8Iyr8v9Q5V+RVrlyZlJSUWIfhXET4+9lFwhdfwNdf2yQ3wXjdAlGtGjz3nDXABwyArl0t5WTIEGt852U878yZ1oifMsUa1A0a2P66dYPjjoOM81Vl/ALw4IP2BeDJJyP3BSAzntPtnHPOOVfMDB9u+c1XXBGb4590klU5efxxK8985pmW9z1xIuzalfP2qpY20rGjTU//+edW8vDXX2HePBs4ffzx+ze4Q6V/AfjyS0tz6doVzj8fVq6M2NPchze6nXPOOeeKkenTrad7yBAb7BgrBxwA114Lv/1mk+mkpcHFF0O9etYDn1khqNRUmDTJ0kI6d7ZG9sMPW2rJvffatrmV/gXg3nttwOWxx9rxI13lxBvdzjnnnHPFyD33wCGHwJVXxjoSU7q05V3PmWP1vWvVssb4kUdaj/z69VaB5KmnbBr6Xr3s/vPPw5Illpud36JRBxxgs9b+/DO0aWPHv+uuyDy/dN7ods4555wrJr7+2lIxbr0VgglI40aJEpZm8vXXdjvhBLj7bmt8165tFVZq1LDc7fnzoV+/yNUWT1e3rs3Q+eqrMHhwZPcd1Ua3iHQWkYUislhEhmbyeAcR2SQis4LbXcHyI0RkmogsEJF5InJdyDbDROTPkG2imPLunMurDh067DcxyKhRo7g6h7pU5cuXB2DlypWcd955We47pzKgo0aN2meSmjPOOIONGzeGE3q2hg0bxsMPP5zv/bjCpai+n13xM3w4HHywDWKMZ23bwjvvWE3vCy6Adu0sJea772yAZHa52vklAj17Ws57JEUtZBEpCYwFugANgF4i0iCTVb9S1WbBbXiwLBW4SVXrA62BQRm2fSxkmw+i9Rycc3nXq1cvJk2atM+ySZMm0atXr7C2P/zww3n99dfzfPyMjZQPPviAypUr53l/rnjz97MrCr77zmagvOWW6NXHjrRGjeCFF+DNN6F9+7xVN4kX0ezpbgksVtUlqroTmAScE86GqrpKVX8Mft8CLABqRi1S51zEnXfeebz33nv8+++/ACxdupSVK1fSrl27PXWGW7RoQePGjXn77bf3237p0qU0atQIsCmte/bsSZMmTbjwwgv3TFUNMHDgQBITE2nYsCF33303AKNHj2blypWcfPLJnHzyyQAkJCSwbt06AB599FEaNWpEo0aNGDVq1J7j1a9fn/79+9OwYUM6deq0z3EyM2vWLFq3bk2TJk3o3r07GzZs2HP8Bg0a0KRJE3r27AnAl19+SbNmzWjWrBnNmzdny5YteX5tXcErqu/nd999l1atWtG8eXNOPfVUVq9eDVgt8H79+tG4cWOaNGmyZxr5jz76iBYtWtC0aVNOOeWUiLy2ruAMH26zMQ4cGOtIiqdo1umuCfwRcn8F0CqT9dqIyGxgJXCzqs4LfVBEEoDmwIyQxYNF5BIgGesR3xDBuAErN1O+vOUQOVfYXX+9jcyOpGbNIPh8z1S1atVo2bIlH330Eeeccw6TJk3iwgsvREQoU6YMU6ZMoWLFiqxbt47WrVtz9tlnI1l0YTz11FOUK1eOOXPmMGfOHFq0aLHnsZEjR1K1alV2797NKaecwpw5c7j22mt59NFHmTZtGtWrV99nXykpKbzwwgvMmDEDVaVVq1a0b9+eKlWqsGjRIl599VWeeeYZLrjgAt544w369OmT5XO85JJLeOKJJ2jfvj133XUX99xzD6NGjeKBBx7g999/58ADD9yTAvDwww8zduxY2rZty9atWykTy5IBhZy/n/fK7/u5Xbt2fP/994gIzz77LA8++CCPPPIII0aMoFKlSswN5uvesGEDa9eupX///kyfPp06derw999/5/HVdrEwc6blKj/wQPQngXGZi2ZPd2Znm4xzzv8I1FbVpsATwFv77ECkPPAGcL2qbg4WPwXUBZoBq4BHMj24yJUikiwiyWvXrs1V4Js3Q2KiFUt3zuVd6CX50Evxqsrtt99OkyZNOPXUU/nzzz/39LBlZvr06XsaC02aNKFJkyZ7Hps8eTItWrSgefPmzJs3j/nz52cb09dff0337t056KCDKF++PD169OCrr74CoE6dOjRr1gyA4447jqVLl2a5n02bNrFx40bat28PwKWXXsr06dP3xNi7d28mTpy4Z6bAtm3bcuONNzJ69Gg2btzoMwgWQkXx/bxixQpOP/10GjduzEMPPcS8edbv9emnnzJo0KA961WpUoXvv/+ek046iTp16gBQtWrVbGNz8WX4cKtLHfJndQUsmmf9FcARIfdrYb3Ze4Q0pFHVD0TkSRGprqrrRKQ01uBOUtU3Q9bbcyYTkWeA9zI7uKqOB8YDJCYmZmzsZ6tiRbjwQpsi9P777b5zhVl2PXjR1K1bN2688UZ+/PFH/vnnnz09eklJSaxdu5aUlBRKly5NQkICO3bsyHZfmfUa/v777zz88MP88MMPVKlShb59++a4H9WsTwcHhkzLVrJkyRzTS7Ly/vvvM336dN555x1GjBjBvHnzGDp0KF27duWDDz6gdevWfPrppxx77LF52n9x5+/nvfL7fr7mmmu48cYbOfvss/niiy8YNmzYnv1mjDGzZa5wmDED3n/fJowJxva6GIhmT/cPQD0RqSMiBwA9gXdCVxCRQyX4DxaRlkE864NlzwELVPXRDNuEjiXtDvwcjeCvvhq2boWXX47G3p0rHsqXL0+HDh247LLL9hlwtmnTJg4++GBKly7NtGnTWLZsWbb7Oemkk0hKSgLg559/Zs6cOQBs3ryZgw46iEqVKrF69Wo+/PDDPdtUqFAh07zpk046ibfeeovt27ezbds2pkyZwoknnpjr51apUiWqVKmyp1fx5Zdfpn379qSlpfHHH39w8skn8+CDD7Jx40a2bt3Kb7/9RuPGjRkyZAiJiYn88ssvuT5mrIRRiaqSiLwrIrODilP9Qh5bKiJzg2pT2ZfoiHNF8f28adMmata0IVMvvfTSnuWdOnVizJgxe+5v2LCBNm3a8OWXX/L7778DeHpJIbFtm9XArlkz8iXwXO5ErdGtqqnAYGAqNhBysqrOE5EBIpJeqOY84Ocgp3s00FPta3tb4GKgYyalAR8MTuBzgJOBG6IRf8uWlmIydqxNNeqcy5tevXoxe/bsPQMKAXr37k1ycjKJiYkkJSXl2OM7cOBAtm7dSpMmTXjwwQdp2bIlAE2bNqV58+Y0bNiQyy67jLZt2+7Z5sorr6RLly57Bp6la9GiBX379qVly5a0atWKK664gubNm+fpub300kvccsstNGnShFmzZnHXXXexe/du+vTpQ+PGjWnevDk33HADlStXZtSoUTRq1IimTZtStmxZunTpkqdjFrQwK1ENAuYHqYIdgEeCzpZ0JwfVphILIuZoKmrv52HDhnH++edz4okn7pMvfuedd7Jhw4Y979lp06ZRo0YNxo8fT48ePWjatCkXXnhh2MdxsXPDDbBokXUiFvcr90lJkJBg5QYTEux+QZLsLk0VFYmJiZpTDdTMvPiiFV7//HPIcJ5zLu4tWLCA+vXrxzoMlwuZ/c1EJCWWjVURaQMMU9XTg/u3Aajq/SHr3IalEw4CEoBPgP+oapqILAUSVXVduMfM7Jzt7+fCw/9W8WPKFOjRw6Z7f+CBWEcTW0lJNgNn6NTy5crB+PHQu3dkj5XVedtnpMzGhRdC1arw5JOxjsQ552Ims0pUGUu4jgHqY+N25gLXqWpa8JgCH4tIiohkOel0fga/O+f29+efcMUVcNxxNoiyuLvjjn0b3GD377ij4GLwRnc2ypaFyy+3b4p//hnraJxzLibCqUR1OjALOByrLDVGRNIvZLdV1RZYesogETkps4Oo6nhVTVTVxBo1akQodOeKp7Q0y+PesQNeeSXyU6VHSkGmeyxfnrvl0eCN7hwMGGBv3vHjYx2Jc7lXHNLHioo4/lvlWIkK6Ae8qWYx8DtwLICqrgx+rgGmYBOn5Ukcv0Yu4H+j+PDww5YaO3o0/Oc/sY4mc+npHsuW2di5ZcvsfrQa3kcembvl0eCN7hwcdRR06WKN7p07Yx2Nc+ErU6YM69ev9w/BQkBVWb9+fbxOmJNjJSpgOXAKgIgcAhwDLBGRg0SkQrD8IKATeaw45e/n+Bfn7+NiIyXFUibOPRcuuyzW0WStoNM9Ro60HO5Q5crZ8oyi1QPvszOEYdAg6NrV0kx8sLYrLGrVqsWKFSvw/NjCoUyZMtSqVSvWYexHVVNFJL0SVUng+fRKVMHjTwMjgBdFZC6WjjIkmG/hKGBKUBm2FPCKql7GIxgAACAASURBVH6Ulzj8/Vw4xOv7uLjYtg0uuggOPdQ6C+O5rHq46R5JSdYQX77ceqVHjszbwMf0bXLaV8YBl+k98KH7yCuvXhKGtDSoVw9q1YIvv4xgYM45l4NYVy+Jhfyes50r7FauhMceg59+gr59rcOvdOmct+vfH557zlJLOnSIdpT5k5BgDdqMateG9MlTC7LiSG7iyolXL8mHEiVg4ECYPh3mzo11NM4555wrihYutIojderAo4/C4sVw8cXW8ffEE/unY4R680149lkrDxjvDW4IL90jFhVHojng0hvdYerXD8qU8fKBzjnnnIusmTMtB7t+fevdveIKm9BmyRJ45x270n7ttdbbOnw4rF+/7/Z//mm93ImJcM89sXkOudW7t/VY165taTC1a+/fgx2LiiPRHHDpje4wVasGPXvajE6bNsU6Guecc84VZqowdapNvteqlaWE3H67pTaMHWuFHEqUgLPOgq+/hq++gtat4e67rYF6ww3wxx97ywP++298lwfMTO/elrKRlmY/M6aMxKLiSG4GXOaWN7pzYdAgG6QwYUKsI3HOOedcYfXOO9CiBXTubD3aDz9svbf33gsHH5z5Nu3awbvvWpprjx4wZow1zE88cW95wHr1CvZ5RFs0G8BZCacHPq98IGUutWoFmzfD/PnxPSrYOVc0+EBK54qWlBQ4/nirnz1kiDXm8tI7vXy55X0/8wycfbb1chfFdkmkqpcUJB9IGSFXXw2//ALTpsU6Euecc84VJqpwzTVQowbMmGHjxfKaDnLkkTBqFKxbBxMnFs0GN+ScgpKuIGe3zCtvdOfShRdafvfYsbGOJL588QW89lqso3DOOefiV1ISfPcd3H8/VKoUmX2WLQslS0ZmX4VVQc9umVfe6M6lMmXg8svh7bdhxYpYRxMf3nwTTjvNvn2uWxfraJxzzrn4s2UL3HqrVRjp2zfW0RQtsSgtmBfe6M6DAQPsMse4cbGOJPZefx0uuMBy03btspwy55xzzu3rvvtg1Sqrt13CW18RFYvSgnnhf/Y8qFPHpoV/5hnYuTPW0cTO5MlWRrFVK7tc1qIFvPBCrKNyzjnn4suiRTbo8ZJLrOyfi6xYlBbMC29059HVV8Pq1ZZaURxNmgQXXQRt2sBHH0HFinDZZTBrlk1b65xzzjlz4402YPKBB2IdSdEUi9KCeeGN7jw6/XSoW7d4DqhMSrL87bZt4cMPoUIFW96rFxx4IDz/fGzjc8455+LFhx/Ce+/BXXfBYYfFOpqiKZq1tSPJG915VKKElf35+mubQaoYlDsHbEbOSy6B9u3hgw+gfPm9j1WtCt26WaN8x47Yxeicc87Fg5074frrbdzTddfFOpqiLdzSgrHkje58GDzYStLcf7/9M6WlxTqi6HrpJbj0Upuy9r334KCD9l/nsstgwwabbcs555wrzp54An79FR57rHBNz+6iI6qNbhHpLCILRWSxiAzN5PEOIrJJRGYFt7ty2lZEqorIJyKyKPhZJZrPIStJSZZeMn685TM/8QRccQXs3p23/aWm2j9mvHr+eSvif+qpNg1txtypdKecAkcc4Skmzjnnire//oJ77rHCC2ecEetoXDyIWqNbREoCY4EuQAOgl4g0yGTVr1S1WXAbHsa2Q4HPVLUe8Flwv0CFFmEHmxa+dGmr3NGrV+4rmsycaVPCHnNMfPYQP/us1SY/7TSrT162bNbrlixp9Uc//hj++CNvx0tKskGaxSVlxznnXNFz222WavnYY7GOxMWLaPZ0twQWq+oSVd0JTALOicC25wAvBb+/BHSLYMxhyawI+65dUKWKzcrYowf880/O+9m0yfLCW7eGNWvg6KOtKsrmzfmPcdu2/O8DrBRg//7QpUvODe50fftag3nChNwfb80aGDQIXn0Vpk3L/fbOOedcrM2cCS++aFVL6tWLdTQuXkSz0V0TCO3rXBEsy6iNiMwWkQ9FpGEY2x6iqqsAgp8HZ3ZwEblSRJJFJHnt2rX5eR77yarY+saNNmHOBx/Y5aStWzNfT9UmlWnQwKqfXHMNLFhgPbwrV9q34/yYMMG+AHz0Uf72o2qDRA85xL5MlCkT3nZHHQUdOliKSW7z3O+4w74wVK5sKTvOOedcYZKWZp/rhx0WfzMiutiKZqNbMlmWMWHgR6C2qjYFngDeysW22VLV8aqaqKqJNWrUyM2mOcquCPuVV1qFj+nTLR1jw4Z911m6FM46C84/3xqzM2bA449bXnjLljYg88knrSpKXsybZzNm7toFN91kueJ59dln8MUX1vDObNBkdi67DJYsga++Cn+b5GR47jl7DQYOtFSb9BQe55xzrjCYMMF6uv/7370ldZ2D6Da6VwBHhNyvBawMXUFVN6vq1uD3D4DSIlI9h21Xi8hhAMHPNdEJP2s5FWHv3dt6hn/80Sp9rFljjeCHH4aGDa0h++ije3O5Q40YYfUl+/fPfdm9bdtsSvYKFayXeP78vM8QqWrf0I84Aq66Kvfbn3uuxRHu8dN7Bg4+2GqZDhxotTaffDL3x3bOOediYfNmGDrUJo6Lx5J1Lrai2ej+AagnInVE5ACgJ7DPMEEROVREJPi9ZRDP+hy2fQe4NPj9UuDtKD6HTIVThL17d6vy8euvcNJJ1ri+5Rar7jF/PtxwA5Qqtf++y5eHp5+GX36B++7LXVzpaSoTJ1pe9AknWAM2L/nd77xjXwruvtsmvMmtcuVsivjXXgsvR33iRPj+e+sZqFjRGvvdutkgznDy451z0RNGJapKIvJukCo4T0T6ZXi8pIj8JCLvFVzUzhWsBQsstXTNGhg92ubzcG4fqhq1G3AG8CvwG3BHsGwAMCD4fTAwD5gNfA+ckN22wfJqWNWSRcHPqjnFcdxxx2msfPWVasWKqjVrqr75pmpaWnjb9emjWqqU6pw54a0/YYIqqN55595l33xjy+65J3cx796t2rixar16qrt25W7bUN99Z8d/5pns19u0SfXQQ1Vbt7Zjp5s2zbZ/7rm8x6Cqum1b/rZ3LpaAZI3ieTqnG1AyOA8fBRwQnK8bZFjnduC/we81gL+BA0IevxF4BXgvnGPG8pztXG5t326fvaVLq1apovrSS7GOyMVaVuftmJ3IC/IW6xP4mjW5b/itXatavbpqq1aqqanZrzt/vmq5cqonnbR/I/ncc1UPOkh11arwj/3KK/bOeOWV3MWcUVqaav36qieckP16N9+sKqI6c+b+2zdqpNqsWfhfVjL6v/+zk+Aff+Rte+diLQ4a3W2AqSH3bwNuy7DObcCT2HicOsBioETwWK2gg6SjN7pdUfPxx6p169pnZp8+qqtXxzoiFw+yOm/7xY8CUKNG1pPJZKV6dRtgOWMGjBmT9Xrbt1sed7ly8Mor+6es3H8//PsvDBsW3nF37bKUlMaN4cILcxdzRiI2oc6331q6TGZ++QVGjbKBlxnz20UsZWbWLNtHbs2fb89/w4bwn79zbj/hVKIaA9THxt7MBa5T1fTaRaOAW4EiPmevK05Wr7aU0k6dLI3k00+tiMLBmdZTc854ozuO9epl9bHvuMOqnmTmuuvg558tJ7pmJgUZ69WzQYnPPmv5Zjl56SVYvBjuvTcy+WgXX2wT5mQ2oFIVrr/eKqNklb/eu3feygeqWl57hQoWwwsvhPf8nXP7Caea1OnALOBwoBkwRkQqisiZwBpVTcnxIFEs8+pcpKSlWWngY4+10r933w1z5th4Ledy4o3uOCYCTz1lvw8YsP8Mja+8Yo3p226D00/Pej933WUN2yFDsj/ev//C8OFWuvCss/IXe7pDD7WBJRMm7F++8N13YepUmyY3q96Bgw6yXvA33rAa5uF69VWrEnP//fDII7Yfr5fqXJ7kWIkK6Ae8GVxZXQz8DhwLtAXOFpGl2CRnHUVkYmYH0SiWeXUuEubOhXbt7PO4eXNrbA8bFv4cFs55ozvO1a5tvcBTp1ojO93ChVbKr107ayhnp3p1a5i/+y58+WXW640bZ1O3jxxpDf5I6dcP/vpr38l6duywCi4NGtgsnNkZNAh277b4wrFpk9UoT0yEK66w9J5bboEpU6xCinMuV3KsRAUsB04BEJFDgGOAJap6m6rWUtWEYLvPVbVPwYXuXP5t22adVs2bw6JFdkX4s8/gmGNiHZkrbLzRXQgMGgStWlkqydq1VkLvgguslN+rr2ZeejCj666zMnw335z5LJHbtllj++STI3+ZrGtX68l+/vm9yx55xCbPGT0aSpfOfvujjrJ9jBsHO3fmfLxhwyzf7qmnLLUFrIF/yCF24sx4xcA5lzVVTcUqTU0FFgCTVXWeiAwQkQHBaiOAE0RkLjZocoiqrotNxM5Fzvvv2/waDz4IffvaOKRLLolsx5QrPrzRXQiULGkzNW7ebI3HG26wy1oTJkCtWuHto2xZy9NOTob//W//x0ePttqike7lBmtUX3yx9bSvWWO96ffdZxPohNvAHzzYGtKvvZb9enPmWP73VVdZT3e68uXh//7PZgr98MO8PxfniiNV/UBV/6OqdVV1ZLDsaVV9Ovh9pap2UtXGqtpIVfdLIVHVL1T1zIKO3bm8+PNPOO88OPNMS0+cPt3SOatVi3VkrjDzRneUJSVBQoINSkxIsPt50bChpYgkJVmP7623whln5G4fffpAs2Y2rfu//+5dvnGjfYvv2tVm0YqGfv0spzspyWJPS7MZOsN12mnwn/9kP6AyLc1SVapU2Ts7aKj+/a3X/LbbMu/td845V7zt3m2fM/XrWy/3yJHw009w4omxjqxwiVTbp8jJrI5gUbvFqubrxIlWP9sSGuxWrpwtz4sdO6xmdfv2qjt35m0fn3xicTzyyN5ld95py376KW/7DFfLlqo1atixhg3L/fajR9u2Get5p3vhBc1xMp30GuQvv5z74zsXC8S4Tncsbl6n28VCSopqYqJ9Rpx+uurixbGOqHCKdNunMMrqvC32WNGWmJioycnJBX7chARYtmz/5bVrZ10CMCc7d1oOd37K+XXubFO8//ab9T4fdZT1mmeWdhJJ48bZqO/ata18X9myudt+82Yri9ijhw1kCbVhgw1qOfpo+PrrrF+ftDRLO9mwwXLz8jLFvXMFSURSVDUx5zWLjlids13xtGWLpR8+8YQNvB81yuap8LztvIlG26ewyeq87eklUbR8ee6Wh+OAA/JfP/vBBy2lZORIeOABm2Dnnnvyt89w9Oxp5Qifeir3DW6AihXh0kth0iTLDQ91552wfj08+WT2r0+JEvacly4NvxqKc865okcV3nzTUklGj7axQL/8Yp9V3uDOu2i0fYoKb3RH0ZFH5m55QWnSxEZhP/EEjB1rI7GPPTb6x61UyWbY7NIl7/sYPNh6+599du+ylBRryA8ebDnrOTntNOjYEUaMsN5z55xzxcuyZXDOOTagv1o1m/X4ySdtMjaXP/Ha9okH3uiOopEj95/+vVy5zAf5FbQRI6wqSlqazahVWBx7LJx6qjWyU1P3Dp48+OCc65WnE7He7nXrrHShc8654mHXLhvE36CB1dp+6CGr6tW6dawjKzriue0Ta97ojqLevWH8eMtjErGf48fb8lirWRNeftlKESYkxDqa3LnmGlixAt56y3q8Z860k2ilSuHv4/jj4fzzrdG9enX0YnXOORcfvv/exvTccouVq50/3+auyGmuCJc78dz2iTUfSOkKnd27bcBk9eo2wU7jxjBtWu5z8H791Xo7Bg7MvhShc7HkAymd29/zz8Pbb9sA+mOP3XurWnX/dTdutFKx48bB4Yfb+b5bN8/bdtGT1Xk7jLkMnYsvJUtaSsmtt1oll7Fj83by/M9/bJr4ceNswqGjjop8rFlZvhzuuAPGjMldD71zzhV3O3ZYb3VaGnz00b4zFdeosW9DvEwZS6dcuxauvdZ+r1AhdrG74s3TS1yhdPnlNuDl5ptt4qC8uusua7j/3/9FLrZwPPwwTJwIr79esMd1zrnC7vXX4e+/7ef27bB4Mbz3np1X03uw337bPh8GD4YjjoAffrBSgN7gdrHkPd2uUKpa1XqLy5fP334OPxyuvx7uv996uxML4CL+tm1764y/9ZZ9gXDOOReeceMsxfDkk60MbN26duvadd/1/v7bxv80bGhXSJ2LNe/pjgM+XWreVKgQmZy8W2+1S5Lt21sFlO3b87/P7Lz6qpUqbN0aPvnEJmZwzjmXs3nzbAK0K6/Mec6KqlWtRK43uF288EZ3jCUl2clj2TIr1L9smd33hnfBqVzZKqB07WrlE485Bl55xf4e0fD009CokZUt/Pdfy0l0zjmXs/HjrdpI376xjsS53PNGd4zdccf+Pavbt9tyV3ASEmDyZJg+3Wp+9+4NJ5xgk/lE0g8/2GQ+AwdCu3ZWgeWttyJ7DOecK4r++QcmTIAePezqpHOFTVQb3SLSWUQWishiERmazXrHi8huETkvuH+MiMwKuW0WkeuDx4aJyJ8hj50RzecQbT5danw58URrGD//vE0V37o1XHwx/PlnZPb/1FNw0EHQp49d8jz7bHj//X1H3zvnnNvf5MlW/u+qq2IdiXN5E7VGt4iUBMYCXYAGQC8RaZDFev8FpqYvU9WFqtpMVZsBxwHbgSkhmz2W/riqfhCt51AQfLrU+FOiBPTrZ3W8b78dXnvNygvmN997wwaYNMka3BUr2rJu3WDTJqsz7pxzLmvjx9u5uEOHWEfiXN5Es6e7JbBYVZeo6k5gEnBOJutdA7wBrMliP6cAv6nqsuiEGVs+XWr8qlDB/g4LFuzN927YENZk9U7NwUsv2eXRAQP2LjvtNOv5njIl6+2cc664+/ln+PZbG/Pkk9q4wiqaje6awB8h91cEy/YQkZpAd+DpbPbTE3g1w7LBIjJHRJ4XkSqZbSQiV4pIsogkr127NvfRFxCfLjX+1aljlzU/+8zSTG6+Off7ULUBlK1bQ7Nme5eXKQNdulhN2bS0yMXsnHNFybhxcMABcOmlsY7EubyLZqM7s++iGetBjAKGqOruTHcgcgBwNvBayOKngLpAM2AV8Ehm26rqeFVNVNXEGnE+4qJ3b8sfTkuzn97gjk8dO8KQIfDyy7lPB/niC1i40AZQZtS9O/z1V+QHbTrnXFGwfbudd887zwafO1dYRbPRvQI4IuR+LWBlhnUSgUkishQ4D3hSRLqFPN4F+FFVV6cvUNXVqrpbVdOAZ7A0FucKxO2323TxAwdaub9wPfWU1Yw9//z9HzvjDJsV01NMnHNuf//7n4198QGUrrCLZqP7B6CeiNQJeqx7Au+ErqCqdVQ1QVUTgNeBq1U1tIBaLzKklojIYSF3uwM/RyN45zJTtiyMHWu91g89FN42q1ZZg7pvX9s+o8qVrRd9ypTo1QZ3DkBEBmeVkudcvBo3DurXt+pSzhVmUWt0q2oqMBirSrIAmKyq80RkgIgMyH5rEJFywGnAmxkeelBE5orIHOBk4IYIh+5ctjp3hgsugHvvhcWLc17/uecgNXXfAZQZde9u+5o/P3JxOpeJQ4EfRGRyUNI1rCFpOZV/FZFKIvKuiMwWkXki0i9YXkZEZoYsvyfCz8cVcbNnW+qdD6B0RYFoMehaS0xM1OTk5FiH4YqQlSut56V1a5tRMqsPg927bSDmMcfYlO9ZWbUKDj8cRoyAO++MTsyucBKRFFVNjOD+BOgE9MNS/CYDz6nqb1msXxL4FesEWYFdxeylqvND1rkdqKSqQ0SkBrAQa+DvAg5S1a0iUhr4GrhOVb/PLkY/Z7t0V19t8yasXGkpes4VBlmdt31GSufy4PDDrZzgxx9bHe+svP8+/PFH5gMoQx12mDXgPa/bRZtaT8tfwS0VqAK8LiIPZrFJOOVfFagQNOjLA38DqWq2BuuUDm5Fv6fHRcS2bTBxol1Z9Aa3Kwq80e1cHg0cCMcdB9ddZ4N8MvP009ZAP+usnPfXvTv8+KPPRuqiR0SuFZEU4EHgG6Cxqg7EJiE7N4vNciz/CowB6mOD5edivdlpwTFLisgsbC6GT1Q10zo9haXMqys4kybBli2WWuJcUeCN7kIkKQkSEmzGxIQEu+9ip2RJG+CzZk3mKSG//26pJ1dcAaVL57y/7t3t51tvZb+ec/lQHeihqqer6muqugsgaCCfmcU24ZR/PR2YBRyOlXMdIyIVg33vDmYXrgW0FJFGmR2kMJV5dQVj3Dho0ADato11JM5Fhje6C4mkJPu2v2yZVbhYtszue8M7to47DgYNsoomGVNQx42zL0j9+4e3r3r1bMZLTzFxUfQBlvoBgIhUEJFWAKq6IIttwin/2g94M0gnWQz8DhwbuoKqbgS+ADrn5wm44uGnn+CHH6xMoA+gdEWFN7oLiTvusAkCQm3fbstdbI0YAYceah8Ou4Npnv7916qWnHUW1KoV/r66dYPp02HduujE6oq9p4CtIfe3Bcuyk2P5V2A5cAqAiBwCHAMsEZEaIlI5WF4WOBX4Jd/PwhV548bZjL0XXxzrSJyLHG90FxJZ5fl6/m/sVaoEo0ZZPvaTT9qyN96whnNOAygz6t7dZiZ9773Ix+kcVrFqT2pIkFZSKrsNwiz/OgI4QUTmAp9hMw2vAw4DpgUlXn/Acrr93e2ytWWLXcW98EKo4lXlXRGS7cnWxY8jj7SUksyWu9g7/3wra3XHHXDuuTaAsm5dOPXU3O2nRQs44oi9k+k4F2FLRORa9vZuXw0syWkjVf0AS00JXfZ0yO8rsTKEGbebAzTPT8Cu+Hn1Vdi61WegdEWP93QXEiNHQrly+y4rV86Wu9gTsbzuXbvgvPPgq6/sA6NELv/DRCzF5OOPrVyWcxE2ADgB+BPL1W4FeG0IF1fGj4fGja2MqnNFiTe6C4neve1EVLu2Ncxq17b7vXvHOjKXrm5dq2Ly3Xdw4IHQr1/e9tO9O+zYAVOnRjY+51R1jar2VNWDVfUQVb1IVdfEOi7n0s2dCykpPoDSFU1hpZeISF1ghar+KyIdgCbAhGA0uisgvXt7Izve3XyzTZbTujVUr563fZx4IlSrZikmPXpENj5XvIlIGeByoCFQJn25ql4Ws6CcC/HNN/aza9fYxuFcNITb0/0GsFtEjgaeA+oAr0QtKpdnXss7tg480AZUPpVTPYhslCplVU/ee8/SVZyLoJex6dlPB77Eyv9tiWlEzoVISbHZJ2vXjnUkzkVeuI3utGAEe3dglKregI1Kd3HEa3nHhxIl8n9ZtFs32LgRvvgiIiE5l+5oVf0/YJuqvgR0BRrHOCbn9khOhsRETy1xRVO4je5dItILuBRIL/cUxhx7riB5Le+io1MnGyjrs1O6CEu/drIxmBmyEpAQu3Cc22vHDvj5Z5t0zMUnv5qeP+E2uvsBbYCRqvq7iNQBJkYvLJcXXsu76ChbFjp3tkZ3Wlqso3FFyHgRqQLciU1wMx/4b2xDcs7MmQOpqd7ojld+NT3/wmp0q+p8Vb1WVV8NTtgVVPWBKMfmcimrmt1ey7tw6t4dVq60qZCdyy8RKQFsVtUNqjpdVY8KqpiMi3VszoHlc4Oll7j441fT8y+sRreIfCEiFUWkKjAbeEFEHo1uaC63vJZ30dK1qw2qfP31WEfiioJg9snBsY7DuawkJ1vlJu8oik9+NT3/wk0vqaSqm4EewAuqehyQy7n2XLR5Le+ipUoVq2Ly6KP2d3QuAj4RkZtF5AgRqZp+i3VQzoH1dPsgysgLNw87p/X8anr+hdvoLiUihwEXsHcgpYtDvXvD0qWWB7x0adYNbh8MUThMmGC53VddBXfdZXl0zuXDZcAgYDqQEtySYxqRc8A///ggymgINw87nPX8anr+hdvoHg5MBX5T1R9E5ChgUfTCctHkgyEKj/Ll4e234fLLYcQI++m1u11eqWqdTG5HxTou5+bMgd27PZ870sLNww5nPb+ann+ixaDrLDExUZOTvTMnXUKCNbQzql3besdd/FGFe+6xW+fONutl+fKxjsoVBBFJUdWINEVE5JLMlqvqhEjsP1L8nF38PPkkDBpkn02erhA5JUpkfoVUZN/KWOGu58KT1Xk73IGUtURkioisEZHVIvKGiNQKY7vOIrJQRBaLyNBs1jteRHaLyHkhy5aKyFwRmSUiySHLq4rIJyKyKPhZJZzn4PbywRCFjwgMGwbPPAOffAIdOsDq1bGOyhVCx4fcTgSGAWfHMiDnwAZR1qgBRxwR60iKlnDzsD1fu2CEm17yAlbT9XCgJvBusCxLIlISGAt0ARoAvUSkQRbr/RdLX8noZFVtluHbwlDgM1WtB3wW3He54P9chdcVV1i6yYIF0KYN/PprrCNyhYmqXhNy6w80Bw6IdVzOpaRYPrcPooyscPOwPV+7YITb6K6hqi+oampwexGokcM2LYHFqrpEVXcCk4BzMlnvGuANYE2YsZwDvBT8/hLQLcztXMD/uQq3rl1tevitW+GEE+C772IdkSvEtgP1Yh2EK97++QfmzfN87rzIqShCuHnYnq9dMMJtdK8TkT4iUjK49QHW57BNTeCPkPsrgmV7iEhNoDvwdCbbK/CxiKSIyJUhyw9R1VUAwc+DMzu4iFwpIskikrx27docQi1ecvPP5VVO4tPxx8O331pZwY4drffbuZyIyLsi8k5wew9YCPi7x8XU7Nk2iNIrl+ROuEURwq1qFu56Lu9KhbneZcAY4DGsMfwtNjV8djK7SJQxTX8UMERVd8v+15TaqupKETkYqy37i6pODzNeVHU8MB5sUE642xUXvXvn/A+V/g+dPqI5/R86fXsXW0cfbQ3vM8+EHj2sx7tly1hH5eLcwyG/pwLLVHVFrIJxDiyfG7ynO7eyqzjin9HxKdxp4Jer6tmqWiOYNrgbNlFOdlYAoUMiagErM6yTCEwSkaXAecCTItItOObK4OcaYAqWrgKwOqgZTvAz3LQUl0s+5Wv8b8evrAAAIABJREFUq1HDBlaWKwfjfDLvsK1bZ5e0i6HlwAxV/VJVvwHWi0hCbENyxV1KChx8MNSsmfO6bi8vilD4hJtekpkbc3j8B6CeiNQRkQOAnthgzD2CGrEJqpoAvA5crapvichBIlIBQEQOAjoBPwebvQNcGvx+KX5pNGr8H7pwqFgRLrgAJk+GbdtiHU38U7UrArfeGutIYuI1ILQA2O5gmXMxk5xcNAZRFnQ6phdFKHzy0+jO9t9DVVOBwVhVkgXAZFWdJyIDRGRADvs+BPhaRGYDM4H3VfWj4LEHgNNEZBFwWnDfRYH/Qxce/frZwMo33oh1JPFv6VL4/Xf46qtYRxITpYKB7QAEv3v1Ehcz27fD/PmFP7UkFpPOeVGEwic/je4c86RV9QNV/Y+q1lXVkcGyp1V1v4GTqtpXVV8Pfl+iqk2DW8P0bYPH1qvqKapaL/j5dz6eg8uG/0MXHm3bWo73iy/GOpL4l17t5eefi2WKyVoR2VOXW0TOAdbltFFOcy6ISKVgkOZsEZknIv2C5UeIyDQRWRAsvy6iz8YVerNm2cC9wj6IMhbpmF5xpPDJttEtIltEZHMmty1YzW5XhIX7Dx3uJTWvhBI9ItC3L0ybZr24Lmvff28/d++2qgnFzADgdhFZLiLLgSHAVdltEOacC4OA+araFOgAPBKkFaYCN6lqfaA1MCiz+Rpc8ZWSYj8Le093rNIxveJI4ZJto1tVK6hqxUxuFVQ13MonrhDL6R863Etqsbj0VtxcfLE1vifE1YTe8ee776BeUJk6/QO/uFDV31S1NdZ4bqiqJ6jq4hw2C2fOBQUqiJWhKg/8DaSq6ipV/TE49hYs1dCHy7k9kpPhkEPg8ELejefpmC4c+UkvcS7sS2peCSX6jjwSTjnFUkzS0nJcvVj65x+7nH3eeVYtIb1UWXEhIveJSGVV3aqqW0Skiojcm8NmOc65gJWUrY9VqJoLXKeq+7wLgyopzYEZWcTmcysUQykp1std2AdR5iYd06/6Fl/e6Hb5Eu4lNa+EUjD69bMrEtPDrmhfvKSkQGoqtGljOaTFrdENdFHVjel3VHUDcEYO24Qz58LpwCws7bAZMEZEKu7ZgUh5bObh61V1c2YHUdXxqpqoqok1auQ04bErCrZtgwULCn8+N+QuHdOv+hZf3uh2+RLuJTW/9FYwunWzEoLRHlD5yy8wdWp0jxEN6YMoW7e23rX58/e/AlPElRSRA9PviEhZ4MBs1ofw5lzoB7ypZjHwO3BscIzSWIM7SVXfzGf8rghJH0RZ2PO504WTXx3uVV/vDS+avNHt8iXcS2peCaVglCsHPXvCa6/Bli3ROcbOnda4P+ec6B0jWr77DurWtUmFEhPtw3HWrFhHVaAmAp+JyOUicjnwCfBSDtvkOOcCNunOKQAicghwDLAkyPF+Dligqo9G8Hm4IiB9TEVR6OkOVzhXfb03vOjyRrfLl3AvqXlpo4LTt6/1nLz+enT2P2oULFwI//4L72RsesUxVWt0t2lj99M/6IvTYEpVfRC4F8u/bgB8BNTOYZtw5lwYAZwgInOBz4AhqroOaAtcDHQUkVnBLad0FldMJCfDYYcV/kGUuRHOVV8fA1V0iWqO5bYLvcTERE0uhsmbrnhShfr1baBgpHO7//wTjjkGTj7ZeoibNy88De9ly+wy7dixcPXV9jodfjh06gQv5dTXG0MikqKqEbsALyLNgIuAC7A0kDdUdUyk9h8Jfs4uHho2hKOOgnffjXUkBSe9Fzu0UV2u3L6dUCVK2PkpIxEfJF9YZHXe9p5uF1c8jy3/0mt2f/UVLM6pGFwu3XyzDUR8/HE4/3zL6964Meft4kFoPjfY65SYWDwGU4rIf0TkLhFZgFUa+QPrdDk53hrcrnjYutUGUcY6n7ugP3PCuerrY6CKLm90u7jheWyRc/HF9iESyZrd06bBpEkwZIj1Tl14oeV3v/125I4RTd9/D2XLQpMme5cdd5wNCt26NXZxFZBfsJzrs1S1nao+AeyOcUyuGJs1y87zscznzs1nTiQb5zkNuPQxUEWXN7pd3PA8tsipWXNv2kQkLkfu2gXXXGMfNkODScBbtrRemsmT87//gvDdd3D88VAqZFqvghpMqQp//x3dY+TgXOAvYJqIPCMip5B5KUDnCkT6FaZYNrpzU0mkIDuEfAxU0eWNbhc3clPL29NQcta3r71206blf19jxsC8eTaIsmxZWyYCF1wAH38c8wZljnbsgJ9+2juIMl36B360U0y++QYOPRQ+/zy6x8mKqk5R1QuxMn5fADcAh4jIUyLSKTZRueIsJcXGVBx2WOxiCPczJxYdQj69e9HkjW4XN8LNY/M0lPCccw5Urvz/7d17lFTVmffx72MjBBXxAhpFbhpUGB1QSmZNMklMTAwYE3UiIGFMJI7GjESSyYqad02WGl/Wink1mozG2wRMAkq8X0ZG4zAxiQYvjYKo5YUggwgKBBWJF27P+8c+FYrqqq5T3XX61Kn6fdbqVVWnzjm160DvenrXs58Ns2d37zyvvw4XXQTjx8MXv7jzc5Mnhxzvu+/u3msk7amnwmh9adBdqJyQdAWT2bOhT5/w7UCa3P0v7j7X3U8k1NteDFyYbqukFbW3p18qMO5njhZ3k3pR0C0NI24emxYXiOdDH4IpU+DOO+Htt7t+nvPPD+UBf/rTjks1H310yO/+9a+719aklU6iLJb0ZMpNm0IKzqRJsMceyb1Ordx9g7tf7+6fTrst0lreeSeUHU17EmXczxxNbJR6UdAtDSNuHpsWF4jvjDPgvffCYjld8cgj8KtfwXe+AyNGdHzeLIx2L1gA69d3q6mJWrgQhg+H/ffv+NzYsSEASGqhn9tvD4H3tGnJnF8ka55+Ov1JlBD/M0cTG6VeFHRLQ4mTx6bFBeI75hgYNaprKSZbt8L06TB4cOfXbdIk2LYtjKg3qsce65haUpDLhQDg6aeTee3Zs8MfLB/7WDLnF8maRlqJMs5njiY2Sr0o6JbMiTPqoBy8oFCz+49/hJdequ3Y666DJUvgxz+G3XevvN/o0XDooY2bYvLqq2FRn3KpJZDsZMply8ICRdOmdUzNEWlV7e2hwtKHP5x2S+LTxEapBwXdkjlaXKA2//RP0NYGN90U/5i1a+H734fjjoMvfanzfQtVTB5+GN54ozstTUYhn7vSSPf++8NBByUTdN90U5hT8JWv1P/cIlm1aFH6+dwiaVDQLZmkxQXiO+CAUHnkl78MaSBxfO97IQ/53/893gjt5Mnh3+KOO7rX1iQ89liYVDp6dOV9crn6VzDZti3UST/++DCqJyKwcWP41q0RUktEelqiQbeZjTezF81smZlVLEtlZseY2TYzOzV6PNjMfmtmeTN7zsxmFO17sZm9ZmaLo58TknwPkk3KwdvZGWeEFIsFC6rv+/jjMGsWfPvbMHJkvPMfcUTIHW/EhXIWLgxB9a67Vt5n7NgQCHSnykupBQtg1Sr42tfqd06RrCtMotRIt7SiXtV36RozawOuAT4LrAKeNLN73f35MvtdBjxYtHkr8B13f8rM+gGLzOyhomOvdPfLk2q7NIepU1s3yC71hS/APvvAeefBUUdB797hZ9ddO97ecUeoXf3979f2GpMmwSWXwOrV4fhG8MEHoUb3jBmd71cIAJ5+Go49tj6vPWtWuOaltc1FWlkjTaIU6WmJBd3AOGCZuy8HMLN5wEnA8yX7fRO4AzimsMHd1wBrovvvmFkeGFTmWBGJoU+fsMDN9deHD70tW2Dz5h23hftbt4ZUjJtvhn79anuNSZPg4otD0P7NbybyNmr29NPhvVXK5y4onkxZj6D7zTfDgkFnnRWuvYgE7e2hItJ++6XdEpGel2R6ySDg1aLHq6Jtf2Vmg4BTgOsqncTMhgFHAY8XbZ5uZs+Y2Swz27teDZbW1CqL6Jx3XljK/aWX4JVXQrrJ2rXw1luhnOKWLSEP+Z134JRTaj//yJFw5JGNVcWks0Vxig0cGCbZ1msy5S23hFF21eYW2dmiRRrlltaVZNBdbvqVlzy+CrjA3ctO7zKzPQij4N9y943R5muBQ4AxhNHwKyoce7aZtZtZ+7p167rSfmkBcRfRaZXAfJddoFc3vv+aPBkefTSU6WsECxeGfP4DDqi+bz0nU86eHSZuHnVUfc4n0gzefjv80d9T+dyt0m9LdiQZdK8CBhc9PghYXbJPDphnZiuAU4GfmdnJAGa2KyHgnuvuf112w93fcPdt7r4duJGQxtKBu9/g7jl3zw0cOLBe70maTJxFdLS6ZXyTJoXb229Ptx0FnS2KUyqXC3W133yze6+5dGkYMVdtbpGdFRag6omRbvXb0oiSDLqfBEaY2XAz6w2cBtxbvIO7D3f3Ye4+DLgd+Bd3v9vMDPg5kHf3HxcfY2bFY1anAM8m+B6kycVZREerW8Y3YkQY3W2EFJPXXgsj7tVSSwoKgcBTT3XvdWfPDhNSNYlXZGeF9K2eCLrVb0sjSizodvetwHRCVZI8cKu7P2dm55jZOVUO/xhwOvDpMqUBf2RmS83sGeBTwLeTeg/S/OIsoqPVLWszeXIoO7hiRbrteOyxcBt3pLsQCHQnxWTzZpgzJ1SLGTCg6+cRaUaLFoW+tbtfPsdJG1G/LY0oyeoluPt8YH7JtrKTJt39jKL7j1A+Jxx3P72OTZQWN3Nm+MqxeESkdBGdIUPCV5OlWnF1yzgmToQLL4TbboPvfje9dixcGCqHjBkTb/9994Xhw7s3mfL++2HdOtXmFilnyZLuz3MopI0U+uxC2gh0XJVY/bY0Gq1IKS0tziI6Wt2yNgcfDMcck/5COQsXhtHr3r3jHzN2bPeC7tmzw6TNz32u6+doRNUWOjOz/mZ2n5ktiRY0m1b03CwzW2tmSgVsYVu2wMsvh0W0uiNu2oj6bWlECrql5VVbUl6rW9Zu0qQQvP7pT+m8/ubN4avsuKklBblcKKe4YUPtr/n66zB/Ppx+evcqwDSaooXOJgCjgClmVho6nQs87+6jgWOBK6K5PAA3AeN7prXSqJYtC+sAxF3ltpK4aSPqt6URKegWiaFaYC47mzgx3KY12r14caiTHXcSZUF38rp/9atQ57wJa3P/daEzd98MFBY6K+ZAv2gS/B7ABsLKwrj776PH0sLy+XDb3aA7zjycAvXb0mgUdItI3Q0dGgLetILuwqI4tY50dzXodg+pJX//93D44bUdmwFVFzoDrgZGEsrCLgVmRGVdRYAdQXd3fz+UNiJZpqBbRBIxeXIYcX7ppZ5/7cceC0tNDyoNDavYe2845JDa87qfeCIEFU04yg3xFjr7HLAYOJCwcNnVZrZnTS+iBc2aWj4ffif32KN751HaiGSZgm4RScTEidDWBjNmhBzrnrRwYe2pJQVdmUw5ezb07Rv+0GhCcRY6mwbc6cEy4BWgpjFNLWjW3PL57k+iLFDaiGSVgm4RScSgQXDttfDAA+FDcevWnnndNWtCqbBaU0sKcrlw/Pr18fZ/91245RY49VTYs6ax3cyoutAZsBI4DsDM9gcOA5b3aCulYW3fHoLu7uZzi2Sdgm6ROoqzaEMrOessuOKKsCz8WWeFD9+k1booTqlcLtzGzeu+6y7YuLFpU0viLnR2KfBRM1sKLAAucPf1AGZ2C7AQOMzMVpnZmT3/LiRNK1fCe+8p6BZposJWIumKu2hDq/nXfw1B6SWXhJHgq64KuZhJWbgw1Obu6iIcRx8dbtvbq9fb/vOf4fLLwx9Yn/xk114vC6otdObuq4HjKxw7JdnWSaOrV+USkaxT0C1SJ50t2tDKQTfARReFwPvKK6F/f/jBD5J7rYULQ+Dcp0/Xju/fH0aMqD7S/cILYbn3lSth3rzw7YaIdKSgWyRQ0C1SJ3EXbWhFZiHNZONGuPRS6NcvmSXit2wJI9Tf+Eb3zpPLwSOPVH7+oYfCRNE+feDhh7ueyiLSCvJ5GDAg/Ii0Mo3NiNRJ3EUbWjXv2wyuvz6sVnn++eF+vS1ZAu+/3/XKJQVjx8Krr8LatR2fu+YamDAh/Ls+8YQCbpFq6lm5RCTLFHSL1EmcRRsKed//+79hQZVC3nerBN5tbWHlxs9/PoxG33xz98+5YQP8+tdwxhlw4okhuO9uIFxuMuXWrXDuuTB9OpxwAjz6aKgRLCKVucPzz8dPLWnVQQlpDQq6ReokzqINneV9t4reveG228LEw698Be65p7bjt2+HJ58MaSof/SgMHAinnQb33Qef+hT853+GRTi6ozAJs1Cv+623QqD9s5+FtJi77gopMiLSubVr4c034wXdrT4oIc1POd0idTR1aueTJpX3HfTtC/feC5/5TEg3mTs3rAS5eTN88EH523ffDaPLDz4I69aFP2xyOfi3fwvpHsccE0bS62HPPeGww8JI97JlYQR9+XKYNat5SwOKJKEwiXLNmjByvXJlSM2aObNjX6nJ6NLsFHSL9KAhQ8LoTbntpebODR82nX1IZVm/fvBf/xVGvCdOjHfMgAGhjN+ECXD88WGUOym5HNx/P4wbF77q/u//hk98IrnXE2lGhaD7Jz8J8y2gcjlVDUpIs1PQLdKDZs7cuZY3dMz7htap+b3PPvCHP4SAtq0tVAPp0yekoBTfFu4feGDPleYbOzb8O4waFVJXDj64Z15XpJnk8+FbqULAXVBuBLuWQQmRLFLQLdKDCh8w1UawW+lr1r32CkuoN5pp00IJwq9/PdTuFpHa5fMhP7uc0hHsuIMSIlmloFukh1XL+wZ9zdoI9torlDYUka7L52H33eEvf+n4XOkIdtxBCZGsUvUSkQYUt+a3iEijevtteO21UCK0WjnVgqlTYcWKUKVoxQoF3NJcEg26zWy8mb1oZsvM7MJO9jvGzLaZ2anVjjWzfczsITN7ObrdO8n3IJKGODW/C1TXVkQa0QsvhNsvf7l6OVWRVpBY0G1mbcA1wARgFDDFzDqsSRXtdxnwYMxjLwQWuPsIYEH0WKSpxKn5DfHr2iowF5GeVqhcMnKkRrBFINmR7nHAMndf7u6bgXnASWX2+yZwB7A25rEnAb+I7v8CODmJxoukLc6HVJzFdrTghIikIZ8PVYdU+UckSDLoHgS8WvR4VbTtr8xsEHAKcF0Nx+7v7msAotv9yr24mZ1tZu1m1r5u3bouvwmRRhZnwqVWwRSRNOTzcOih0EslG0SAZINuK7OttHDQVcAF7r6tC8d2yt1vcPecu+cGJrmChkiK4ky4VCUUEUlDPh9v+XeRVpFk0L0KGFz0+CBgdck+OWCema0ATgV+ZmYnVzn2DTM7ACC6LU5LEWkpcSZcqhKKiPS099+H5csVdIsUSzLofhIYYWbDzaw3cBpwb/EO7j7c3Ye5+zDgduBf3P3uKsfeC3w1uv9V4J4E34NIQ4sz4bKWSigiIvXw0kthPoqCbpEdEsu0cvetZjadUJWkDZjl7s+Z2TnR86V53FWPjZ7+IXCrmZ0JrAQmJvUeRLKg2mI7WnBCRHpaceUSEQkSnd7g7vOB+SXbygbb7n5GtWOj7X8GjqtfK0WaX5xVMCFUNFFwLiLdlc+Hb98OPTTtlog0Ds0pFhFgR2nBQqWTQmlBUOAtIrXJ50OpwL59026JSOPQMvAiAqi0oFRWbXVhM+tvZveZ2RIze87MpsU9VpqTKpeIdKSgW0SA+KUFtbpla4m5uvC5wPPuPho4FrjCzHrHXZlYmsvWrfDiiwq6RUop6BYRIF5pQa1u2ZLirC7sQD8zM2APYAOwNeax0mReeQU2b1bQLVJKQbeIAPFKCyoFpSVVXV0YuBoYSVhPYSkww923xzwW0CrCzUSVS0TKU9AtIkC8mt9a3bIlxVkh+HPAYuBAYAxwtZntGfPYsFGrCDcNBd0i5al6iYj8VbXSgkOGhJSSctulacVZXXga8EN3d2CZmb0CHB7zWGky+TwceCD07592S0Qai0a6RSQ2rW7ZkqquLkxYqOw4ADPbHzgMWB7zWMmwchOrVblEpDyNdItIbFrdsvXEXF34UuAmM1tKSCm5wN3XA3SyurBkXLna/medFSZZ//M/p9s2kUakkW4RqcnUqbBiBWzfHm4VcDc/d5/v7oe6+yHuPjPadl1hhWF3X+3ux7v7ke5+hLvP6ezYtMQpdxm3JKbOVX5i9Xvvwfvva6RbpCx3b/qfsWPHuog0njlz3IcOdTcLt3PmpN2ixgO0ewP0oz35k0SfPWeO+267uYdx2PCz2247/5+Ls4/OtWMfs52fL/75n/+J/28j0mwq9dsWnmtuuVzO29vb026GSEuZO7fzNJTSr6Yh5IeXVkxpdWa2yN1zabejJyXRZw8bVn4S8NCh4RubuPvoXNXPBbBmDXz4w+WfE2l2lfptBd0iUndxAuq4H/6tTkF3feyySxiDLWUWUqXi7qNz7div3O95r17Qpw+8807YV6QVVeq3ldMtInUXZxGdetf81vL00pk4K67G2Ufn2nG/XG3/j3wERo9WwC1SjoJuEam7OAF13A//OLQ8vVQTp9xl3JKYOtcOpROrN2zQJEqRisolejfbjyZSivSsoUPLT64aOnTHPnEndBX27WzCZZzXyyo0kbJu4kzcjTu5V+fqaP368Ht3+eWd7yfS7Cr128rpFpG6iztJstpky7jnipunmkXK6ZaseOQR+PjH4f774YQT0m6NSHqU0y0iPaZcrme5qiRxan7HyQ+vZ6qKiHRNPh9ulV4iUp6CbhFJRL0W0YmTH67l6UXSl89D377hj2wR6UhBt4g0tK5WUahU77ueK/aJyA75PBx+ePi9EZGOEv3VMLPxZvaimS0zswvLPH+SmT1jZovNrN3M/iHafli0rfCz0cy+FT13sZm9VvScMsdEmlhXqyhUCrirVTlRJRSRrsnnlVoi0pnEgm4zawOuASYAo4ApZjaqZLcFwGh3HwN8DfgPAHd/0d3HRNvHAu8CdxUdd2XheXefn9R7EJH01TKKXU2c/PA4+4jIzjZtCn+gKugWqaxXguceByxz9+UAZjYPOAl4vrCDu28q2n93oFwpleOAP7l7hcVmRaTZTZ1an6Xh4+SH13vRHpFW8OKL4VZBt0hlSaaXDAJeLXq8Ktq2EzM7xcxeAO4njHaXOg24pWTb9CgtZZaZ7V3uxc3s7ChlpX3dunVdewci0lTquWIfKPdbpECVS0SqSzLoLrcIbIeRbHe/y90PB04GLt3pBGa9gS8CtxVtvhY4BBgDrAGuKPfi7n6Du+fcPTdw4MCuvQMRaSr1XLFPud8iO+Tz0KtXWAZeRMpLMuheBQwuenwQsLrSzu7+e+AQMxtQtHkC8JS7v1G03xvuvs3dtwM3EtJYRESqipMfHjeHXLnfIjvk8yHg7t077ZaINK4kc7qfBEaY2XDgNUKayJeLdzCzjxDytd3MjgZ6A38u2mUKJaklZnaAu6+JHp4CPJtQ+0WkCcXJD4+zj3K/RXZQ5RKR6hIb6Xb3rcB04EEgD9zq7s+Z2Tlmdk6025eAZ81sMaHSyeRozXrMbDfgs8CdJaf+kZktNbNngE8B307qPYiIVBI391t538nTNU7X5s3w8ssKukWqSXKkm6ic3/ySbdcV3b8MuKzCse8C+5bZfnqdmykiUrOZM0MOd3GKSWnudyHvu7BPIe8b6lONRcpf4zPPhOXLYcKEdNvWKlauhG3bFHSLVGPRwHJTy+Vy3t7ennYzRKTJzJ0bcrhXrgwj3DNn7hxMDxsWgsBSQ4eGBXziMLNF7p6rR3uzopY+u9I1lp73zDNw5JFpt0IkfZX67URHukVEmlm13G/lfSevs2t53309145Wt9deCrhFqlHQLSKSkCFDyo/CVsoHb1RmNh74CdAG/Ie7/7Dk+e8ChT8/egEjgYHuvsHMZgBnEcrI3ujuV9WzbZWu8dChcOKJ9XwlEZHuSbJkoIhIS4tb87uRmVkbYaL7BGAUMMXMRhXv4+7/z93HuPsY4HvA76KA+whCwD0OGA2caGYj6tm+ZrjGItIaFHSLiCQkbs3vBjcOWObuy919MzAPOKmT/YtLvY4EHnP3d6OKVr8jlHqtmya5xiLSApReIiKSoDg1vxvcIODVosergL8rt2NU6nU8oVwshHUUZprZvsB7wAlA2RmSZnY2cDbAkBrzb5rgGotIC9BIt4iIdMbKbKtU9uoLwKPuvgHA3fOEsrAPAQ8AS4Ct5Q509xvcPefuuYEDB3a/1SIiDUZBt4iIdGYVMLjo8UHA6gr7nkbJKsLu/nN3P9rdPwFsAF5OpJUiIg1OQbeIiHTmSWCEmQ03s96EwPre0p3MrD/wSeCeku37RbdDgH+kJCgXEWkVyukWEZGK3H2rmU0HHiSUDJzl7s+Z2TnR84VVhk8BfuPufyk5xR1RTvcW4Fx3f7On2i4i0kgUdIuISKfcfT4wv2TbdSWPbwJuKnPsx5Nsm4hIVrTEMvBmtg4oXj5hALA+pebUg9qfniy3HdT+NHW17UPdvaVmFqrPbjhqf3qy3HZo3faX7bdbIuguZWbt7p5Lux1dpfanJ8ttB7U/TVlue9qyfu3U/nRluf1Zbjuo/aU0kVJEREREJGEKukVEREREEtaqQfcNaTegm9T+9GS57aD2pynLbU9b1q+d2p+uLLc/y20HtX8nLZnTLSIiIiLSk1p1pFtEREREpMco6BYRERERSVjLBd1mNt7MXjSzZWZ2YdrtqZWZrTCzpWa22Mza025PZ8xslpmtNbNni7btY2YPmdnL0e3eabaxMxXaf7GZvRZd/8VmdkKabazEzAab2W/NLG9mz5nZjGh7Jq5/J+3PyvX/kJk9YWZLovZfEm3PxPVvJOqze1aW++0s99mQ7X5bfXbM12mlnG4zawNeAj4LrAKeBKa4+/OpNqwGZrYCyLl7wxebN7NPAJuAX7r7EdG2HwEb3P2H0Qfo3u5+QZrtrKRC+y+6k6AAAAAFBklEQVQGNrn75Wm2rRozOwA4wN2fMrN+wCLgZOAMMnD9O2n/JLJx/Q3Y3d03mdmuwCPADOAfycD1bxTqs3telvvtLPfZkO1+W312PK020j0OWObuy919MzAPOCnlNjUtd/89sKFk80nAL6L7vyD8UjakCu3PBHdf4+5PRfffAfLAIDJy/TtpfyZ4sCl6uGv042Tk+jcQ9dk9LMv9dpb7bMh2v60+O55WC7oHAa8WPV5Fhv5TRBz4jZktMrOz025MF+zv7msg/JIC+6Xcnq6YbmbPRF9lNtzXfKXMbBhwFPA4Gbz+Je2HjFx/M2szs8XAWuAhd8/k9U+Z+uzGkPX/t5noM4plud9Wn11ZqwXdVmZb1vJrPubuRwMTgHOjr9Ok51wLHAKMAdYAV6TbnM6Z2R7AHcC33H1j2u2pVZn2Z+b6u/s2dx8DHASMM7Mj0m5TBqnPlu7KTJ9RkOV+W31251ot6F4FDC56fBCwOqW2dIm7r45u1wJ3Eb5+zZI3otyvQg7Y2pTbUxN3fyP6xdwO3EgDX/8oL+0OYK673xltzsz1L9f+LF3/And/C3gYGE+Grn+DUJ/dGDL7/zZrfUaW+2312dW1WtD9JDDCzIabWW/gNODelNsUm5ntHk1QwMx2B44Hnu38qIZzL/DV6P5XgXtSbEvNCr98kVNo0OsfTQr5OZB39x8XPZWJ61+p/Rm6/gPNbK/ofl/gM8ALZOT6NxD12Y0hs/9vs9JnQLb7bfXZMV+nlaqXAETlaq4C2oBZ7j4z5SbFZmYHE0ZKAHoBNzdy+83sFuBYYADwBnARcDdwKzAEWAlMdPeGnPhSof3HEr4mc2AF8PVCvlcjMbN/AP4ALAW2R5v/DyHHruGvfyftn0I2rv/fEibdtBEGN2519x+Y2b5k4Po3EvXZPSvL/XaW+2zIdr+tPjvm67Ra0C0iIiIi0tNaLb1ERERERKTHKegWEREREUmYgm4RERERkYQp6BYRERERSZiCbhERERGRhCnolpZmZtvMbHHRz4V1PPcwM2vImqQiIlmlfluyqlfaDRBJ2XvRsq8iIpIN6rclkzTSLVKGma0ws8vM7Ino5yPR9qFmtsDMnoluh0Tb9zezu8xsSfTz0ehUbWZ2o5k9Z2a/iVa6wszOM7Pno/PMS+ltiog0DfXb0ugUdEur61vyNeXkouc2uvs44GrCinhE93/p7n8LzAV+Gm3/KfA7dx8NHA08F20fAVzj7n8DvAV8Kdp+IXBUdJ5zknpzIiJNSP22ZJJWpJSWZmab3H2PMttXAJ929+Vmtivwurvva2brgQPcfUu0fY27DzCzdcBB7v5B0TmGAQ+5+4jo8QXAru7+f83sAWATYXnlu919U8JvVUSkKajflqzSSLdIZV7hfqV9yvmg6P42dsyj+DxwDTAWWGRmml8hItJ96relYSnoFqlsctHtwuj+H4HTovtTgUei+wuAbwCYWZuZ7VnppGa2CzDY3X8LnA/sBXQYtRERkZqp35aGpb/SpNX1NbPFRY8fcPdC+ak+ZvY44Y/TKdG284BZZvZdYB0wLdo+A7jBzM4kjIx8A1hT4TXbgDlm1h8w4Ep3f6tu70hEpLmp35ZMUk63SBlRbmDO3den3RYREalO/bY0OqWXiIiIiIgkTCPdIiIiIiIJ00i3iIiIiEjCFHSLiIiIiCRMQbeIiIiISMIUdIuIiIiIJExBt4iIiIhIwv4/kB+ja0UBpYsAAAAASUVORK5CYII=\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"show_report(X_test, y_test, y_pred)\nshow_matrix(y_test, y_pred)","execution_count":94,"outputs":[{"output_type":"stream","text":"              precision    recall  f1-score   support\n\n           0       0.89      0.94      0.91      1583\n           1       0.69      0.54      0.61       417\n\n    accuracy                           0.85      2000\n   macro avg       0.79      0.74      0.76      2000\nweighted avg       0.84      0.85      0.85      2000\n\n","name":"stdout"},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]}],"metadata":{"kernelspec":{"display_name":"Python 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